Packages
library("tidyverse") # for dplyr workflow + ggplot
library("rmdformats") # pretty Rmd format
library("lme4") # LMM
library("lmerTest") # LMM stats
library("emmeans") # pairwise comparisons
library("broom") # clean stats presentation
library("AICcmodavg") # model selection
library("MuMIn") # GLMM R-squared
library("effects") # partial regression plots
library("ggpubr") # figure arranging & exportingFig Theme
my_ggplot_theme <- theme_classic() + # start with a preset theme to make things easy
theme(axis.title = element_text(size = 10), # text size for axis labels
axis.text = element_text(size = 8, # text size for axis values
color = "black", # need to specifically set color
family = "sans"), # and family
legend.title = element_text(size = 8), # text size for the legend title
legend.text = element_text(size = 8), # text size for legend items
text = element_text(color = "black", # color for all text (except axis values)
family = "sans") # family
)Now, apply the theme. This sets the theme for the entire Rmd document.
Acronyms
DAB = day after birth PS = post-shed / 1 week after birth
Load & Tidy Data
DAB osmolality
This is the osmolality of each individual neonate the day after birth.
## # A tibble: 6 × 3
## # Groups: CEWL_Blood_Collect_Date [1]
## CEWL_Blood_Collect_Date Indiv_ID Plasma_Osmol_Rep_mean
## <date> <fct> <dbl>
## 1 2022-08-28 300 306
## 2 2022-08-28 301 312
## 3 2022-08-28 302 304
## 4 2022-08-28 303 313
## 5 2022-08-28 304 293
## 6 2022-08-28 305 278DAB body size
This is the body size and body temperature at time of CEWL measurements for each individual neonate.
neonate_dab_all0 <- read_rds("data/neonate_DAB_body_size.RDS")
neonate_dab_all <- neonate_dab_all0 %>%
# fix a typo of where the decimal was put
mutate(SVL_cm = case_when(SVL_cm == 2.4 ~ SVL_cm*10, TRUE ~ SVL_cm)) %>%
rename(Date_Born_Shed = Date_Born) %>%
mutate(timept = "DAB")
# robin removed this one...
#neonate_dab_all[77, ]
summary(neonate_dab_all)## Indiv_ID Date_Born_Shed Tail_Length_cm SVL_cm
## 300 : 1 Min. :2022-08-26 Min. :1.200 Min. :20.50
## 301 : 1 1st Qu.:2022-08-30 1st Qu.:1.600 1st Qu.:22.00
## 302 : 1 Median :2022-09-01 Median :1.900 Median :22.55
## 303 : 1 Mean :2022-08-31 Mean :1.829 Mean :22.67
## 304 : 1 3rd Qu.:2022-09-04 3rd Qu.:2.000 3rd Qu.:23.15
## 305 : 1 Max. :2022-09-05 Max. :2.500 Max. :25.70
## (Other):76
## Tail_SVL_Ratio Mass_g Sex Mother_ID Treatment Tb_CEWL_c
## Min. :0.05330 Min. :11.6 F:27 101 : 5 C:36 Min. :25.80
## 1st Qu.:0.06825 1st Qu.:13.5 M:55 124 : 5 W:46 1st Qu.:27.23
## Median :0.08480 Median :14.9 125 : 5 Median :28.25
## Mean :0.09035 Mean :15.4 126 : 5 Mean :28.23
## 3rd Qu.:0.09150 3rd Qu.:16.5 127 : 5 3rd Qu.:29.00
## Max. :0.87500 Max. :23.0 128 : 5 Max. :31.30
## (Other):52
## Pee CEWL_Blood_Collect_Date timept
## N:39 Min. :2022-08-28 Length:82
## Y:43 1st Qu.:2022-08-31 Class :character
## Median :2022-09-02 Mode :character
## Mean :2022-09-01
## 3rd Qu.:2022-09-05
## Max. :2022-09-06
## PS osmolality
first, separate out and get mean osmolality per individual
postshed_osmol <- neonate_postshed %>%
select(CEWL_Blood_Collect_Date, Indiv_ID,
Plasma_Osmol_Rep1, Plasma_Osmol_Rep2,
Plasma_Osmol_Rep3, Plasma_Osmol_Rep4) %>%
pivot_longer(3:6, names_to = "rep", values_to = "osml") %>%
filter(complete.cases(osml)) %>%
group_by(CEWL_Blood_Collect_Date, Indiv_ID) %>%
summarise(Plasma_Osmol_Rep_mean = mean(osml))
head(postshed_osmol)## # A tibble: 6 × 3
## # Groups: CEWL_Blood_Collect_Date [1]
## CEWL_Blood_Collect_Date Indiv_ID Plasma_Osmol_Rep_mean
## <date> <fct> <dbl>
## 1 2022-09-03 303 352
## 2 2022-09-03 304 345
## 3 2022-09-03 344 349
## 4 2022-09-03 345 332.
## 5 2022-09-03 346 344
## 6 2022-09-03 347 428PS body size
neonate_postshed_clean <- neonate_postshed %>%
select(Indiv_ID, Date_Born_Shed = Date_Shed,
CEWL_Blood_Collect_Date,
Tail_Length_cm, SVL_cm, Tail_SVL_Ratio,
Mass_g, Sex, Pee, Tb_CEWL_c,
Mother_ID, Treatment) %>%
mutate(timept = "PS") %>%
# fix a typo
mutate(Treatment = case_when(Mother_ID == 128 ~ "W", TRUE ~ Treatment)) %>%
mutate(Treatment = factor(Treatment))
summary(neonate_postshed_clean)## Indiv_ID Date_Born_Shed CEWL_Blood_Collect_Date Tail_Length_cm
## 303 : 1 Min. :2022-09-02 Min. :2022-09-03 Min. :1.50
## 304 : 1 1st Qu.:2022-09-02 1st Qu.:2022-09-03 1st Qu.:1.65
## 320 : 1 Median :2022-09-03 Median :2022-09-04 Median :1.90
## 344 : 1 Mean :2022-09-03 Mean :2022-09-04 Mean :1.88
## 345 : 1 3rd Qu.:2022-09-04 3rd Qu.:2022-09-05 3rd Qu.:2.05
## 346 : 1 Max. :2022-09-04 Max. :2022-09-05 Max. :2.30
## (Other):29
## SVL_cm Tail_SVL_Ratio Mass_g Sex Pee
## Min. :22.40 Min. :0.06220 Min. :11.10 F:13 N:29
## 1st Qu.:23.15 1st Qu.:0.06855 1st Qu.:12.05 M:22 Y: 6
## Median :23.80 Median :0.08160 Median :13.00
## Mean :23.87 Mean :0.07881 Mean :13.42
## 3rd Qu.:24.00 3rd Qu.:0.08665 3rd Qu.:14.40
## Max. :26.50 Max. :0.09750 Max. :19.00
##
## Tb_CEWL_c Mother_ID Treatment timept
## Min. :26.50 125 :5 C:19 Length:35
## 1st Qu.:27.75 128 :5 W:16 Class :character
## Median :28.50 133 :5 Mode :character
## Mean :28.63 134 :5
## 3rd Qu.:29.55 135 :5
## Max. :30.60 129 :4
## (Other):6CEWL
all_data_CEWL0 <- read_rds("data/CEWL_data_all.RDS")
all_data_CEWL <- all_data_CEWL0 %>%
rename(CEWL_Blood_Collect_Date = date)
summary(all_data_CEWL)## CEWL_Blood_Collect_Date Indiv_ID CEWL_g_m2h msmt_temp_C
## Min. :2022-08-22 103 : 4 Min. : 4.043 Min. :22.86
## 1st Qu.:2022-08-29 115 : 4 1st Qu.:12.500 1st Qu.:24.95
## Median :2022-09-02 101 : 3 Median :15.350 Median :25.68
## Mean :2022-08-31 102 : 3 Mean :16.404 Mean :25.49
## 3rd Qu.:2022-09-05 104 : 3 3rd Qu.:20.636 3rd Qu.:26.12
## Max. :2022-09-06 105 : 3 Max. :30.150 Max. :27.40
## (Other):199
## msmt_RH_percent reps e_s_kPa e_a_kPa
## Min. :32.92 Min. :3.000 Min. :2.784 Min. :1.141
## 1st Qu.:39.16 1st Qu.:4.000 1st Qu.:3.157 1st Qu.:1.265
## Median :41.27 Median :4.000 Median :3.296 Median :1.342
## Mean :41.14 Mean :4.174 Mean :3.265 Mean :1.341
## 3rd Qu.:42.76 3rd Qu.:4.000 3rd Qu.:3.385 3rd Qu.:1.409
## Max. :49.68 Max. :8.000 Max. :3.648 Max. :1.693
##
## VPD_kPa
## Min. :1.488
## 1st Qu.:1.836
## Median :1.927
## Mean :1.923
## 3rd Qu.:2.025
## Max. :2.324
## Mother Data!
mother_values <- read_rds("data/mother_values.RDS")
neo_counts <- read_csv("data/neonate_count.csv",
col_types = "fddd") %>%
mutate(
total_embryos = (live_neonates+dead_at_birth+slugs),
prop_live = live_neonates/total_embryos
)
summary(neo_counts)## Mother_ID live_neonates dead_at_birth slugs
## 133 : 1 Min. : 5.000 Min. :0.0000 Min. :0.0000
## 112 : 1 1st Qu.: 6.500 1st Qu.:0.0000 1st Qu.:0.0000
## 115 : 1 Median : 8.000 Median :0.0000 Median :0.0000
## 119 : 1 Mean : 8.105 Mean :0.4737 Mean :0.3684
## 126 : 1 3rd Qu.: 9.500 3rd Qu.:1.0000 3rd Qu.:1.0000
## 114 : 1 Max. :13.000 Max. :2.0000 Max. :2.0000
## (Other):13
## total_embryos prop_live
## Min. : 6.000 Min. :0.6250
## 1st Qu.: 7.500 1st Qu.:0.8229
## Median : 9.000 Median :1.0000
## Mean : 8.947 Mean :0.9109
## 3rd Qu.:10.000 3rd Qu.:1.0000
## Max. :16.000 Max. :1.0000
## ## # A tibble: 9 × 6
## Mother_ID live_neonates dead_at_birth slugs total_embryos prop_live
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 133 13 1 2 16 0.812
## 2 126 10 1 0 11 0.909
## 3 125 8 2 0 10 0.8
## 4 128 8 2 0 10 0.8
## 5 129 8 0 1 9 0.889
## 6 134 8 0 1 9 0.889
## 7 105 6 1 1 8 0.75
## 8 117 5 2 1 8 0.625
## 9 131 5 0 1 6 0.833## Mother_ID live_neonates dead_at_birth slugs total_embryos
## 133 :1 Min. : 5.000 Min. :0 Min. :0.0000 Min. : 6.000
## 126 :1 1st Qu.: 6.000 1st Qu.:0 1st Qu.:0.0000 1st Qu.: 8.000
## 125 :1 Median : 8.000 Median :1 Median :1.0000 Median : 9.000
## 128 :1 Mean : 7.889 Mean :1 Mean :0.7778 Mean : 9.667
## 129 :1 3rd Qu.: 8.000 3rd Qu.:2 3rd Qu.:1.0000 3rd Qu.:10.000
## 134 :1 Max. :13.000 Max. :2 Max. :2.0000 Max. :16.000
## (Other):3
## prop_live
## Min. :0.6250
## 1st Qu.:0.8000
## Median :0.8125
## Mean :0.8120
## 3rd Qu.:0.8889
## Max. :0.9091
## Join/Arrange Dataframes
DAB
All DAB: missing CEWL for neonate 332
dab_all <- neonate_dab_all %>%
left_join(neonate_dab_osmol,
by = c('Indiv_ID', 'CEWL_Blood_Collect_Date')) %>%
left_join(all_data_CEWL,
by = c('Indiv_ID', 'CEWL_Blood_Collect_Date')) %>%
# remove one erroneous point
filter(Plasma_Osmol_Rep_mean < 400) %>%
left_join(mother_values, by = "Mother_ID") %>%
left_join(neo_counts, by = "Mother_ID")
summary(dab_all)## Indiv_ID Date_Born_Shed Tail_Length_cm SVL_cm
## 300 : 1 Min. :2022-08-26 Min. :1.200 Min. :20.50
## 301 : 1 1st Qu.:2022-08-30 1st Qu.:1.600 1st Qu.:22.00
## 302 : 1 Median :2022-09-01 Median :1.900 Median :22.50
## 303 : 1 Mean :2022-08-31 Mean :1.835 Mean :22.66
## 304 : 1 3rd Qu.:2022-09-04 3rd Qu.:2.000 3rd Qu.:23.20
## 305 : 1 Max. :2022-09-05 Max. :2.500 Max. :25.70
## (Other):75
## Tail_SVL_Ratio Mass_g Sex Mother_ID Treatment
## Min. :0.05330 Min. :11.60 F:26 101 : 5 C:35
## 1st Qu.:0.06870 1st Qu.:13.50 M:55 124 : 5 W:46
## Median :0.08520 Median :14.90 125 : 5
## Mean :0.09071 Mean :15.41 126 : 5
## 3rd Qu.:0.09170 3rd Qu.:16.50 127 : 5
## Max. :0.87500 Max. :23.00 128 : 5
## (Other):51
## Tb_CEWL_c Pee CEWL_Blood_Collect_Date timept
## Min. :25.80 N:39 Min. :2022-08-28 Length:81
## 1st Qu.:27.30 Y:42 1st Qu.:2022-08-31 Class :character
## Median :28.30 Median :2022-09-02 Mode :character
## Mean :28.25 Mean :2022-09-01
## 3rd Qu.:29.00 3rd Qu.:2022-09-05
## Max. :31.30 Max. :2022-09-06
##
## Plasma_Osmol_Rep_mean CEWL_g_m2h msmt_temp_C msmt_RH_percent
## Min. :263.0 Min. : 4.043 Min. :23.56 Min. :32.92
## 1st Qu.:283.7 1st Qu.: 9.841 1st Qu.:25.28 1st Qu.:38.85
## Median :293.0 Median :12.359 Median :26.00 Median :40.84
## Mean :295.7 Mean :11.995 Mean :25.78 Mean :40.82
## 3rd Qu.:304.0 3rd Qu.:13.926 3rd Qu.:26.48 3rd Qu.:42.57
## Max. :355.0 Max. :23.808 Max. :27.20 Max. :49.42
## NA's :1 NA's :1 NA's :1
## reps e_s_kPa e_a_kPa VPD_kPa
## Min. :3.000 Min. :2.904 Min. :1.141 Min. :1.566
## 1st Qu.:4.000 1st Qu.:3.219 1st Qu.:1.294 1st Qu.:1.883
## Median :4.000 Median :3.360 Median :1.353 Median :1.959
## Mean :4.188 Mean :3.320 Mean :1.354 Mean :1.966
## 3rd Qu.:4.000 3rd Qu.:3.455 3rd Qu.:1.416 3rd Qu.:2.077
## Max. :8.000 Max. :3.606 Max. :1.693 Max. :2.324
## NA's :1 NA's :1 NA's :1 NA's :1
## Mother_CEWL Mother_mass Mother_SVL Mother_osml
## Min. :10.27 Min. :181.7 Min. :72.00 Min. :281.7
## 1st Qu.:16.96 1st Qu.:281.2 1st Qu.:75.50 1st Qu.:304.0
## Median :19.90 Median :319.7 Median :80.50 Median :311.7
## Mean :19.25 Mean :337.7 Mean :81.24 Mean :312.7
## 3rd Qu.:21.25 3rd Qu.:379.0 3rd Qu.:86.50 3rd Qu.:320.8
## Max. :27.82 Max. :570.9 Max. :91.00 Max. :357.2
##
## Mother_Days_in_Treatment live_neonates dead_at_birth slugs
## Min. : 2.000 Min. : 5.000 Min. :0.0000 Min. :0.0000
## 1st Qu.: 5.000 1st Qu.: 7.000 1st Qu.:0.0000 1st Qu.:0.0000
## Median : 7.000 Median : 8.000 Median :0.0000 Median :0.0000
## Mean : 8.333 Mean : 8.284 Mean :0.4198 Mean :0.3457
## 3rd Qu.:14.000 3rd Qu.:10.000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :15.000 Max. :13.000 Max. :2.0000 Max. :2.0000
##
## total_embryos prop_live
## Min. : 6.000 Min. :0.7500
## 1st Qu.: 7.000 1st Qu.:0.8333
## Median : 9.000 Median :1.0000
## Mean : 9.049 Mean :0.9238
## 3rd Qu.:10.000 3rd Qu.:1.0000
## Max. :16.000 Max. :1.0000
## PS
All PS:
B vs P
subset of neonates/clutches with measurements at BOTH time points:
dab_ps_compare <- dab_all %>%
# only keep clutches with post-shed msmts
filter(Mother_ID %in% ps_all$Mother_ID) %>%
# remove some cols that do not have matches in ps data
select(-prop_live, -total_embryos, -slugs, -dead_at_birth, -live_neonates, -Mother_Days_in_Treatment, -Mother_osml, -Mother_SVL, -Mother_mass, -Mother_CEWL) %>%
# add those post-shed data
rbind(ps_all) %>%
# formatting
mutate(timept = factor(timept))
summary(dab_ps_compare)## Indiv_ID Date_Born_Shed Tail_Length_cm SVL_cm
## 303 : 2 Min. :2022-08-26 Min. :1.200 Min. :20.5
## 304 : 2 1st Qu.:2022-08-28 1st Qu.:1.625 1st Qu.:22.3
## 320 : 2 Median :2022-08-30 Median :1.950 Median :23.0
## 300 : 1 Mean :2022-08-31 Mean :1.858 Mean :23.1
## 301 : 1 3rd Qu.:2022-09-03 3rd Qu.:2.000 3rd Qu.:24.0
## 302 : 1 Max. :2022-09-04 Max. :2.500 Max. :26.5
## (Other):65
## Tail_SVL_Ratio Mass_g Sex Mother_ID Treatment
## Min. :0.05330 Min. :11.10 F:24 125 :10 C:39
## 1st Qu.:0.06945 1st Qu.:12.82 M:50 128 :10 W:35
## Median :0.08405 Median :14.15 133 :10
## Mean :0.08059 Mean :14.37 134 :10
## 3rd Qu.:0.08930 3rd Qu.:15.28 135 :10
## Max. :0.10330 Max. :20.00 129 : 9
## (Other):15
## Tb_CEWL_c Pee CEWL_Blood_Collect_Date timept Plasma_Osmol_Rep_mean
## Min. :25.80 N:54 Min. :2022-08-28 DAB:39 Min. :242.7
## 1st Qu.:27.62 Y:20 1st Qu.:2022-08-29 PS :35 1st Qu.:286.6
## Median :28.50 Median :2022-08-31 Median :296.8
## Mean :28.61 Mean :2022-09-01 Mean :304.3
## 3rd Qu.:29.60 3rd Qu.:2022-09-04 3rd Qu.:315.9
## Max. :31.30 Max. :2022-09-05 Max. :428.0
##
## CEWL_g_m2h msmt_temp_C msmt_RH_percent reps
## Min. : 4.745 Min. :23.56 Min. :35.15 Min. :3.000
## 1st Qu.:10.897 1st Qu.:25.32 1st Qu.:38.77 1st Qu.:4.000
## Median :13.725 Median :25.73 Median :40.25 Median :4.000
## Mean :13.829 Mean :25.64 Mean :40.51 Mean :4.192
## 3rd Qu.:16.712 3rd Qu.:26.10 3rd Qu.:42.20 3rd Qu.:4.000
## Max. :23.808 Max. :27.20 Max. :46.22 Max. :8.000
## NA's :1 NA's :1 NA's :1 NA's :1
## e_s_kPa e_a_kPa VPD_kPa
## Min. :2.904 Min. :1.148 Min. :1.566
## 1st Qu.:3.228 1st Qu.:1.258 1st Qu.:1.906
## Median :3.305 Median :1.330 Median :1.960
## Mean :3.292 Mean :1.332 Mean :1.960
## 3rd Qu.:3.380 3rd Qu.:1.400 3rd Qu.:2.059
## Max. :3.606 Max. :1.532 Max. :2.179
## NA's :1 NA's :1 NA's :1Clutch Avgs
Get min/max, sd, and mean per clutch per variable.
DAB Proportions
dab_all_props <- dab_all %>%
#group_by(Mother_ID, Treatment, timept, Sex) %>%
#count() %>%
group_by(Mother_ID, Treatment, timept) %>%
summarise(
n_neonates = n(), # number MEASURED, not n birthed
Prop_Pee = sum(Pee == "Y")/n_neonates,
Prop_F = sum(Sex == "F")/n_neonates
)
summary(dab_all_props)## Mother_ID Treatment timept n_neonates Prop_Pee
## 101 : 1 C: 8 Length:18 Min. :3.0 Min. :0.0000
## 103 : 1 W:10 Class :character 1st Qu.:4.0 1st Qu.:0.2875
## 104 : 1 Mode :character Median :5.0 Median :0.6000
## 105 : 1 Mean :4.5 Mean :0.5287
## 112 : 1 3rd Qu.:5.0 3rd Qu.:0.7875
## 114 : 1 Max. :5.0 Max. :1.0000
## (Other):12
## Prop_F
## Min. :0.0000
## 1st Qu.:0.2000
## Median :0.3667
## Mean :0.3296
## 3rd Qu.:0.5000
## Max. :0.7500
## DAB Means
dab_all_clutch_avg <- dab_all %>%
group_by(Mother_ID, Treatment, timept) %>%
mutate(n = n()) %>%
group_by(Mother_ID, Treatment, timept, n) %>%
summarise_at(
vars(Tail_Length_cm, SVL_cm, Mass_g, Tb_CEWL_c,
Plasma_Osmol_Rep_mean, CEWL_g_m2h,
msmt_temp_C, msmt_RH_percent, VPD_kPa),
list(mean = mean, sd = sd, min = min, max = max),
na.rm = T
)
length(unique(dab_all_clutch_avg$Mother_ID)) == nrow(dab_all_clutch_avg)## [1] TRUE## Mother_ID Treatment timept n Tail_Length_cm_mean
## 101 : 1 C: 8 Length:18 Min. :3.0 Min. :1.560
## 103 : 1 W:10 Class :character 1st Qu.:4.0 1st Qu.:1.706
## 104 : 1 Mode :character Median :5.0 Median :1.877
## 105 : 1 Mean :4.5 Mean :1.833
## 112 : 1 3rd Qu.:5.0 3rd Qu.:1.945
## 114 : 1 Max. :5.0 Max. :2.100
## (Other):12
## SVL_cm_mean Mass_g_mean Tb_CEWL_c_mean Plasma_Osmol_Rep_mean_mean
## Min. :21.80 Min. :12.80 Min. :26.32 Min. :280.5
## 1st Qu.:22.08 1st Qu.:13.91 1st Qu.:27.76 1st Qu.:284.8
## Median :22.43 Median :14.92 Median :28.09 Median :297.8
## Mean :22.67 Mean :15.27 Mean :28.24 Mean :296.6
## 3rd Qu.:23.07 3rd Qu.:16.06 3rd Qu.:28.73 3rd Qu.:302.7
## Max. :24.94 Max. :22.30 Max. :29.92 Max. :324.7
##
## CEWL_g_m2h_mean msmt_temp_C_mean msmt_RH_percent_mean VPD_kPa_mean
## Min. : 5.005 Min. :23.71 Min. :34.38 Min. :1.595
## 1st Qu.:10.705 1st Qu.:25.42 1st Qu.:38.78 1st Qu.:1.902
## Median :12.709 Median :26.03 Median :40.85 Median :1.959
## Mean :12.076 Mean :25.83 Mean :40.68 Mean :1.976
## 3rd Qu.:14.229 3rd Qu.:26.33 3rd Qu.:41.42 3rd Qu.:2.094
## Max. :15.892 Max. :26.88 Max. :46.73 Max. :2.293
##
## Tail_Length_cm_sd SVL_cm_sd Mass_g_sd Tb_CEWL_c_sd
## Min. :0.1633 Min. :0.2708 Min. :0.2380 Min. :0.3768
## 1st Qu.:0.2527 1st Qu.:0.4820 1st Qu.:0.3880 1st Qu.:0.5765
## Median :0.2791 Median :0.6630 Median :0.6510 Median :0.8608
## Mean :0.2780 Mean :0.6670 Mean :0.8053 Mean :0.9451
## 3rd Qu.:0.3125 3rd Qu.:0.8281 3rd Qu.:1.0364 3rd Qu.:1.2762
## Max. :0.4359 Max. :1.0954 Max. :2.0768 Max. :1.8050
##
## Plasma_Osmol_Rep_mean_sd CEWL_g_m2h_sd msmt_temp_C_sd msmt_RH_percent_sd
## Min. : 1.250 Min. :0.7602 Min. :0.1313 Min. :0.05774
## 1st Qu.: 6.050 1st Qu.:1.6437 1st Qu.:0.1575 1st Qu.:0.38341
## Median : 8.307 Median :2.0524 Median :0.1925 Median :0.58473
## Mean :10.125 Mean :2.4926 Mean :0.2216 Mean :0.82751
## 3rd Qu.:11.687 3rd Qu.:2.8243 3rd Qu.:0.2910 3rd Qu.:1.03296
## Max. :34.588 Max. :5.4595 Max. :0.3538 Max. :2.60736
##
## VPD_kPa_sd Tail_Length_cm_min SVL_cm_min Mass_g_min
## Min. :0.009671 Min. :1.200 Min. :20.50 Min. :11.60
## 1st Qu.:0.026563 1st Qu.:1.400 1st Qu.:21.32 1st Qu.:12.80
## Median :0.040532 Median :1.500 Median :21.60 Median :13.40
## Mean :0.043391 Mean :1.506 Mean :21.79 Mean :14.26
## 3rd Qu.:0.056447 3rd Qu.:1.600 3rd Qu.:22.43 3rd Qu.:14.90
## Max. :0.110722 Max. :1.900 Max. :24.00 Max. :21.50
##
## Tb_CEWL_c_min Plasma_Osmol_Rep_mean_min CEWL_g_m2h_min msmt_temp_C_min
## Min. :25.80 Min. :263.0 Min. : 4.043 Min. :23.56
## 1st Qu.:26.82 1st Qu.:280.4 1st Qu.: 8.108 1st Qu.:25.24
## Median :27.05 Median :285.8 Median : 9.804 Median :25.82
## Mean :27.22 Mean :284.8 Mean : 9.534 Mean :25.57
## 3rd Qu.:27.65 3rd Qu.:288.9 3rd Qu.:10.988 3rd Qu.:26.06
## Max. :29.50 Max. :302.5 Max. :12.994 Max. :26.70
##
## msmt_RH_percent_min VPD_kPa_min Tail_Length_cm_max SVL_cm_max
## Min. :32.92 Min. :1.566 Min. :2.000 Min. :22.40
## 1st Qu.:37.81 1st Qu.:1.837 1st Qu.:2.000 1st Qu.:23.00
## Median :40.20 Median :1.918 Median :2.050 Median :23.30
## Mean :39.78 Mean :1.923 Mean :2.122 Mean :23.46
## 3rd Qu.:41.22 3rd Qu.:2.040 3rd Qu.:2.275 3rd Qu.:23.80
## Max. :45.88 Max. :2.214 Max. :2.500 Max. :25.70
##
## Mass_g_max Tb_CEWL_c_max Plasma_Osmol_Rep_mean_max CEWL_g_m2h_max
## Min. :13.30 Min. :27.30 Min. :282.0 Min. : 5.973
## 1st Qu.:14.53 1st Qu.:28.62 1st Qu.:293.1 1st Qu.:12.828
## Median :16.00 Median :29.45 Median :307.2 Median :15.102
## Mean :16.13 Mean :29.46 Mean :307.9 Mean :15.398
## 3rd Qu.:17.05 3rd Qu.:30.27 3rd Qu.:315.5 3rd Qu.:17.650
## Max. :23.00 Max. :31.30 Max. :355.0 Max. :23.808
##
## msmt_temp_C_max msmt_RH_percent_max VPD_kPa_max
## Min. :24.02 Min. :36.30 Min. :1.641
## 1st Qu.:25.64 1st Qu.:39.56 1st Qu.:1.933
## Median :26.29 Median :41.69 Median :2.004
## Mean :26.09 Mean :41.71 Mean :2.024
## 3rd Qu.:26.59 3rd Qu.:42.69 3rd Qu.:2.143
## Max. :27.20 Max. :49.42 Max. :2.324
## Add Mother Data!
dab_clutch_mom <- dab_all_clutch_avg %>%
left_join(mother_values, by = "Mother_ID") %>%
left_join(dab_all_props, by = c("Mother_ID", "Treatment", "timept")) %>%
left_join(neo_counts, by = "Mother_ID") %>%
mutate(Mother_mass_kg = Mother_mass/1000)
summary(dab_clutch_mom)## Mother_ID Treatment timept n Tail_Length_cm_mean
## 101 : 1 C: 8 Length:18 Min. :3.0 Min. :1.560
## 103 : 1 W:10 Class :character 1st Qu.:4.0 1st Qu.:1.706
## 104 : 1 Mode :character Median :5.0 Median :1.877
## 105 : 1 Mean :4.5 Mean :1.833
## 112 : 1 3rd Qu.:5.0 3rd Qu.:1.945
## 114 : 1 Max. :5.0 Max. :2.100
## (Other):12
## SVL_cm_mean Mass_g_mean Tb_CEWL_c_mean Plasma_Osmol_Rep_mean_mean
## Min. :21.80 Min. :12.80 Min. :26.32 Min. :280.5
## 1st Qu.:22.08 1st Qu.:13.91 1st Qu.:27.76 1st Qu.:284.8
## Median :22.43 Median :14.92 Median :28.09 Median :297.8
## Mean :22.67 Mean :15.27 Mean :28.24 Mean :296.6
## 3rd Qu.:23.07 3rd Qu.:16.06 3rd Qu.:28.73 3rd Qu.:302.7
## Max. :24.94 Max. :22.30 Max. :29.92 Max. :324.7
##
## CEWL_g_m2h_mean msmt_temp_C_mean msmt_RH_percent_mean VPD_kPa_mean
## Min. : 5.005 Min. :23.71 Min. :34.38 Min. :1.595
## 1st Qu.:10.705 1st Qu.:25.42 1st Qu.:38.78 1st Qu.:1.902
## Median :12.709 Median :26.03 Median :40.85 Median :1.959
## Mean :12.076 Mean :25.83 Mean :40.68 Mean :1.976
## 3rd Qu.:14.229 3rd Qu.:26.33 3rd Qu.:41.42 3rd Qu.:2.094
## Max. :15.892 Max. :26.88 Max. :46.73 Max. :2.293
##
## Tail_Length_cm_sd SVL_cm_sd Mass_g_sd Tb_CEWL_c_sd
## Min. :0.1633 Min. :0.2708 Min. :0.2380 Min. :0.3768
## 1st Qu.:0.2527 1st Qu.:0.4820 1st Qu.:0.3880 1st Qu.:0.5765
## Median :0.2791 Median :0.6630 Median :0.6510 Median :0.8608
## Mean :0.2780 Mean :0.6670 Mean :0.8053 Mean :0.9451
## 3rd Qu.:0.3125 3rd Qu.:0.8281 3rd Qu.:1.0364 3rd Qu.:1.2762
## Max. :0.4359 Max. :1.0954 Max. :2.0768 Max. :1.8050
##
## Plasma_Osmol_Rep_mean_sd CEWL_g_m2h_sd msmt_temp_C_sd msmt_RH_percent_sd
## Min. : 1.250 Min. :0.7602 Min. :0.1313 Min. :0.05774
## 1st Qu.: 6.050 1st Qu.:1.6437 1st Qu.:0.1575 1st Qu.:0.38341
## Median : 8.307 Median :2.0524 Median :0.1925 Median :0.58473
## Mean :10.125 Mean :2.4926 Mean :0.2216 Mean :0.82751
## 3rd Qu.:11.687 3rd Qu.:2.8243 3rd Qu.:0.2910 3rd Qu.:1.03296
## Max. :34.588 Max. :5.4595 Max. :0.3538 Max. :2.60736
##
## VPD_kPa_sd Tail_Length_cm_min SVL_cm_min Mass_g_min
## Min. :0.009671 Min. :1.200 Min. :20.50 Min. :11.60
## 1st Qu.:0.026563 1st Qu.:1.400 1st Qu.:21.32 1st Qu.:12.80
## Median :0.040532 Median :1.500 Median :21.60 Median :13.40
## Mean :0.043391 Mean :1.506 Mean :21.79 Mean :14.26
## 3rd Qu.:0.056447 3rd Qu.:1.600 3rd Qu.:22.43 3rd Qu.:14.90
## Max. :0.110722 Max. :1.900 Max. :24.00 Max. :21.50
##
## Tb_CEWL_c_min Plasma_Osmol_Rep_mean_min CEWL_g_m2h_min msmt_temp_C_min
## Min. :25.80 Min. :263.0 Min. : 4.043 Min. :23.56
## 1st Qu.:26.82 1st Qu.:280.4 1st Qu.: 8.108 1st Qu.:25.24
## Median :27.05 Median :285.8 Median : 9.804 Median :25.82
## Mean :27.22 Mean :284.8 Mean : 9.534 Mean :25.57
## 3rd Qu.:27.65 3rd Qu.:288.9 3rd Qu.:10.988 3rd Qu.:26.06
## Max. :29.50 Max. :302.5 Max. :12.994 Max. :26.70
##
## msmt_RH_percent_min VPD_kPa_min Tail_Length_cm_max SVL_cm_max
## Min. :32.92 Min. :1.566 Min. :2.000 Min. :22.40
## 1st Qu.:37.81 1st Qu.:1.837 1st Qu.:2.000 1st Qu.:23.00
## Median :40.20 Median :1.918 Median :2.050 Median :23.30
## Mean :39.78 Mean :1.923 Mean :2.122 Mean :23.46
## 3rd Qu.:41.22 3rd Qu.:2.040 3rd Qu.:2.275 3rd Qu.:23.80
## Max. :45.88 Max. :2.214 Max. :2.500 Max. :25.70
##
## Mass_g_max Tb_CEWL_c_max Plasma_Osmol_Rep_mean_max CEWL_g_m2h_max
## Min. :13.30 Min. :27.30 Min. :282.0 Min. : 5.973
## 1st Qu.:14.53 1st Qu.:28.62 1st Qu.:293.1 1st Qu.:12.828
## Median :16.00 Median :29.45 Median :307.2 Median :15.102
## Mean :16.13 Mean :29.46 Mean :307.9 Mean :15.398
## 3rd Qu.:17.05 3rd Qu.:30.27 3rd Qu.:315.5 3rd Qu.:17.650
## Max. :23.00 Max. :31.30 Max. :355.0 Max. :23.808
##
## msmt_temp_C_max msmt_RH_percent_max VPD_kPa_max Mother_CEWL
## Min. :24.02 Min. :36.30 Min. :1.641 Min. :10.27
## 1st Qu.:25.64 1st Qu.:39.56 1st Qu.:1.933 1st Qu.:17.18
## Median :26.29 Median :41.69 Median :2.004 Median :19.82
## Mean :26.09 Mean :41.71 Mean :2.024 Mean :19.22
## 3rd Qu.:26.59 3rd Qu.:42.69 3rd Qu.:2.143 3rd Qu.:21.22
## Max. :27.20 Max. :49.42 Max. :2.324 Max. :27.82
##
## Mother_mass Mother_SVL Mother_osml Mother_Days_in_Treatment
## Min. :181.7 Min. :72.00 Min. :281.7 Min. : 2.000
## 1st Qu.:281.4 1st Qu.:75.75 1st Qu.:304.2 1st Qu.: 5.000
## Median :314.3 Median :80.90 Median :312.0 Median : 8.000
## Mean :334.6 Mean :81.32 Mean :313.5 Mean : 8.833
## 3rd Qu.:371.0 3rd Qu.:85.88 3rd Qu.:320.6 3rd Qu.:14.000
## Max. :570.9 Max. :91.00 Max. :357.2 Max. :15.000
##
## n_neonates Prop_Pee Prop_F live_neonates
## Min. :3.0 Min. :0.0000 Min. :0.0000 Min. : 5.000
## 1st Qu.:4.0 1st Qu.:0.2875 1st Qu.:0.2000 1st Qu.: 7.000
## Median :5.0 Median :0.6000 Median :0.3667 Median : 8.000
## Mean :4.5 Mean :0.5287 Mean :0.3296 Mean : 8.278
## 3rd Qu.:5.0 3rd Qu.:0.7875 3rd Qu.:0.5000 3rd Qu.: 9.750
## Max. :5.0 Max. :1.0000 Max. :0.7500 Max. :13.000
##
## dead_at_birth slugs total_embryos prop_live
## Min. :0.0000 Min. :0.0000 Min. : 6.00 Min. :0.7500
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.: 7.25 1st Qu.:0.8472
## Median :0.0000 Median :0.0000 Median : 9.00 Median :1.0000
## Mean :0.3889 Mean :0.3333 Mean : 9.00 Mean :0.9268
## 3rd Qu.:0.7500 3rd Qu.:0.7500 3rd Qu.:10.00 3rd Qu.:1.0000
## Max. :2.0000 Max. :2.0000 Max. :16.00 Max. :1.0000
##
## Mother_mass_kg
## Min. :0.1817
## 1st Qu.:0.2814
## Median :0.3143
## Mean :0.3346
## 3rd Qu.:0.3710
## Max. :0.5709
## PS Means
ps_all_clutch_avg <- ps_all %>%
group_by(Mother_ID, Treatment, timept) %>%
mutate(n = n()) %>%
group_by(Mother_ID, Treatment, timept, n) %>%
summarise_at(
vars(Tail_Length_cm, SVL_cm, Mass_g, Tb_CEWL_c,
Plasma_Osmol_Rep_mean, CEWL_g_m2h,
msmt_temp_C, msmt_RH_percent, VPD_kPa),
list(mean = mean, sd = sd, min = min, max = max),
na.rm = T
)
length(unique(ps_all_clutch_avg$Mother_ID)) == nrow(ps_all_clutch_avg)## [1] TRUEB vs P
dab_ps_compare_means <- dab_all_clutch_avg %>%
# only keep clutches with post-shed msmts
filter(Mother_ID %in% ps_all$Mother_ID) %>%
# add those post-shed data
rbind(ps_all_clutch_avg) %>%
# formatting
mutate(timept = factor(timept))
summary(dab_ps_compare_means)## Mother_ID Treatment timept n Tail_Length_cm_mean
## 124 :2 C:8 DAB:8 Min. :3.000 Min. :1.560
## 125 :2 W:8 PS :8 1st Qu.:4.750 1st Qu.:1.775
## 128 :2 Median :5.000 Median :1.880
## 129 :2 Mean :4.625 Mean :1.865
## 131 :2 3rd Qu.:5.000 3rd Qu.:1.962
## 133 :2 Max. :5.000 Max. :2.160
## (Other):4
## SVL_cm_mean Mass_g_mean Tb_CEWL_c_mean Plasma_Osmol_Rep_mean_mean
## Min. :21.80 Min. :11.66 Min. :27.42 Min. :280.5
## 1st Qu.:22.31 1st Qu.:13.07 1st Qu.:27.96 1st Qu.:286.2
## Median :23.35 Median :14.14 Median :28.60 Median :296.6
## Mean :23.17 Mean :14.40 Mean :28.58 Mean :304.2
## 3rd Qu.:23.72 3rd Qu.:15.07 3rd Qu.:29.23 3rd Qu.:312.5
## Max. :25.87 Max. :18.46 Max. :29.92 Max. :363.4
##
## CEWL_g_m2h_mean msmt_temp_C_mean msmt_RH_percent_mean VPD_kPa_mean
## Min. : 7.296 Min. :23.71 Min. :36.80 Min. :1.595
## 1st Qu.:10.795 1st Qu.:25.40 1st Qu.:39.02 1st Qu.:1.923
## Median :15.046 Median :25.75 Median :40.10 Median :1.962
## Mean :13.959 Mean :25.65 Mean :40.39 Mean :1.965
## 3rd Qu.:16.193 3rd Qu.:26.01 3rd Qu.:41.24 3rd Qu.:2.060
## Max. :17.244 Max. :26.88 Max. :45.57 Max. :2.160
##
## Tail_Length_cm_sd SVL_cm_sd Mass_g_sd Tb_CEWL_c_sd
## Min. :0.1517 Min. :0.4435 Min. :0.2380 Min. :0.3000
## 1st Qu.:0.1930 1st Qu.:0.6132 1st Qu.:0.4584 1st Qu.:0.4959
## Median :0.2334 Median :0.7606 Median :0.6737 Median :0.9314
## Mean :0.2407 Mean :0.7866 Mean :0.8755 Mean :0.9654
## 3rd Qu.:0.2842 3rd Qu.:0.9680 3rd Qu.:1.2115 3rd Qu.:1.3562
## Max. :0.3808 Max. :1.0970 Max. :2.0768 Max. :1.8050
##
## Plasma_Osmol_Rep_mean_sd CEWL_g_m2h_sd msmt_temp_C_sd msmt_RH_percent_sd
## Min. : 2.739 Min. :1.128 Min. :0.1311 Min. :0.1248
## 1st Qu.: 6.355 1st Qu.:1.765 1st Qu.:0.1642 1st Qu.:0.3391
## Median : 8.009 Median :2.213 Median :0.1990 Median :0.6041
## Mean :12.556 Mean :2.577 Mean :0.2269 Mean :0.8738
## 3rd Qu.:14.390 3rd Qu.:3.172 3rd Qu.:0.2876 3rd Qu.:1.2300
## Max. :43.634 Max. :5.040 Max. :0.3929 Max. :2.6074
##
## VPD_kPa_sd Tail_Length_cm_min SVL_cm_min Mass_g_min
## Min. :0.009671 Min. :1.200 Min. :20.50 Min. :11.10
## 1st Qu.:0.028434 1st Qu.:1.500 1st Qu.:21.38 1st Qu.:11.90
## Median :0.037982 Median :1.500 Median :22.45 Median :13.05
## Mean :0.044378 Mean :1.562 Mean :22.18 Mean :13.23
## 3rd Qu.:0.062645 3rd Qu.:1.700 3rd Qu.:23.00 3rd Qu.:14.28
## Max. :0.110722 Max. :1.900 Max. :24.60 Max. :16.60
##
## Tb_CEWL_c_min Plasma_Osmol_Rep_mean_min CEWL_g_m2h_min msmt_temp_C_min
## Min. :25.80 Min. :242.7 Min. : 4.745 Min. :23.56
## 1st Qu.:26.98 1st Qu.:279.5 1st Qu.: 9.571 1st Qu.:25.06
## Median :27.40 Median :286.2 Median :11.494 Median :25.52
## Mean :27.50 Mean :290.2 Mean :10.959 Mean :25.40
## 3rd Qu.:27.93 3rd Qu.:296.2 3rd Qu.:12.982 3rd Qu.:25.76
## Max. :29.50 Max. :332.5 Max. :14.812 Max. :26.70
##
## msmt_RH_percent_min VPD_kPa_min Tail_Length_cm_max SVL_cm_max
## Min. :35.15 Min. :1.566 Min. :2.000 Min. :22.40
## 1st Qu.:37.66 1st Qu.:1.858 1st Qu.:2.000 1st Qu.:23.45
## Median :39.23 Median :1.917 Median :2.050 Median :24.00
## Mean :39.44 Mean :1.911 Mean :2.119 Mean :24.11
## 3rd Qu.:40.96 3rd Qu.:1.972 3rd Qu.:2.200 3rd Qu.:24.62
## Max. :44.34 Max. :2.144 Max. :2.500 Max. :26.50
##
## Mass_g_max Tb_CEWL_c_max Plasma_Osmol_Rep_mean_max CEWL_g_m2h_max
## Min. :12.10 Min. :28.10 Min. :283.5 Min. : 9.705
## 1st Qu.:13.70 1st Qu.:29.35 1st Qu.:297.5 1st Qu.:13.229
## Median :14.70 Median :30.20 Median :306.2 Median :18.466
## Mean :15.25 Mean :29.83 Mean :318.5 Mean :17.158
## 3rd Qu.:16.38 3rd Qu.:30.43 3rd Qu.:327.8 3rd Qu.:19.771
## Max. :20.00 Max. :31.30 Max. :428.0 Max. :23.808
##
## msmt_temp_C_max msmt_RH_percent_max VPD_kPa_max
## Min. :24.02 Min. :37.77 Min. :1.641
## 1st Qu.:25.68 1st Qu.:40.20 1st Qu.:1.963
## Median :25.99 Median :41.31 Median :2.001
## Mean :25.93 Mean :41.46 Mean :2.014
## 3rd Qu.:26.29 3rd Qu.:42.48 3rd Qu.:2.138
## Max. :27.20 Max. :46.22 Max. :2.179
## Exp Info
dab_ps_compare %>%
group_by(Mother_ID) %>%
summarise(
min_date = min(CEWL_Blood_Collect_Date),
max_date = max(CEWL_Blood_Collect_Date)
) %>%
mutate(range = max_date - min_date) %>%
arrange(range)## # A tibble: 8 × 4
## Mother_ID min_date max_date range
## <fct> <date> <date> <drtn>
## 1 124 2022-08-31 2022-09-05 5 days
## 2 131 2022-08-31 2022-09-05 5 days
## 3 133 2022-08-31 2022-09-05 5 days
## 4 134 2022-08-31 2022-09-05 5 days
## 5 128 2022-08-29 2022-09-04 6 days
## 6 129 2022-08-28 2022-09-03 6 days
## 7 135 2022-08-28 2022-09-03 6 days
## 8 125 2022-08-28 2022-09-04 7 daysdab_ps_compare %>%
summarise(
min_date = min(CEWL_Blood_Collect_Date),
max_date = max(CEWL_Blood_Collect_Date)
) %>%
mutate(range = max_date - min_date)## min_date max_date range
## 1 2022-08-28 2022-09-05 8 daysTest Repro Output
##
## Call:
## lm(formula = Prop_Pee ~ Treatment, data = dab_clutch_mom)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4500 -0.3109 0.0750 0.2604 0.4354
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3146 0.1027 3.063 0.00744 **
## TreatmentW 0.3854 0.1378 2.797 0.01292 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2905 on 16 degrees of freedom
## Multiple R-squared: 0.3284, Adjusted R-squared: 0.2864
## F-statistic: 7.823 on 1 and 16 DF, p-value: 0.01292##
## Call:
## lm(formula = Prop_F ~ Treatment, data = dab_clutch_mom)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.39167 -0.16375 -0.01083 0.18625 0.47000
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.39167 0.08315 4.711 0.000236 ***
## TreatmentW -0.11167 0.11155 -1.001 0.331711
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2352 on 16 degrees of freedom
## Multiple R-squared: 0.05894, Adjusted R-squared: 0.0001214
## F-statistic: 1.002 on 1 and 16 DF, p-value: 0.3317##
## Call:
## lm(formula = live_neonates ~ Treatment, data = dab_clutch_mom)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.10 -1.50 -0.10 1.25 4.50
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.500 0.765 11.11 6.22e-09 ***
## TreatmentW -0.400 1.026 -0.39 0.702
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.164 on 16 degrees of freedom
## Multiple R-squared: 0.009405, Adjusted R-squared: -0.05251
## F-statistic: 0.1519 on 1 and 16 DF, p-value: 0.7019##
## Call:
## lm(formula = dead_at_birth ~ Treatment, data = dab_clutch_mom)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.50 -0.45 -0.30 0.30 1.70
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.5000 0.2516 1.988 0.0642 .
## TreatmentW -0.2000 0.3375 -0.593 0.5617
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7115 on 16 degrees of freedom
## Multiple R-squared: 0.02148, Adjusted R-squared: -0.03968
## F-statistic: 0.3512 on 1 and 16 DF, p-value: 0.5617##
## Call:
## lm(formula = slugs ~ Treatment, data = dab_clutch_mom)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.500 -0.425 -0.200 0.325 1.500
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.5000 0.2092 2.390 0.0295 *
## TreatmentW -0.3000 0.2806 -1.069 0.3009
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5916 on 16 degrees of freedom
## Multiple R-squared: 0.06667, Adjusted R-squared: 0.008333
## F-statistic: 1.143 on 1 and 16 DF, p-value: 0.3009##
## Call:
## lm(formula = total_embryos ~ Treatment, data = dab_clutch_mom)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.600 -2.250 -0.500 1.175 6.500
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.5000 0.8856 10.727 1.03e-08 ***
## TreatmentW -0.9000 1.1882 -0.757 0.46
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.505 on 16 degrees of freedom
## Multiple R-squared: 0.03462, Adjusted R-squared: -0.02572
## F-statistic: 0.5737 on 1 and 16 DF, p-value: 0.4598still_neo_mom_mass_lm <- lm(data = dab_clutch_mom,
dead_at_birth ~ Treatment * Mother_mass)
car::Anova(still_neo_mom_mass_lm, type = "2", test = "F")## Anova Table (Type II tests)
##
## Response: dead_at_birth
## Sum Sq Df F value Pr(>F)
## Treatment 0.3236 1 0.5845 0.4573
## Mother_mass 0.3477 1 0.6281 0.4413
## Treatment:Mother_mass 0.0016 1 0.0028 0.9585
## Residuals 7.7507 14slug_mom_mass_lm <- lm(data = dab_clutch_mom,
slugs ~ Treatment * Mother_mass)
car::Anova(slug_mom_mass_lm, type = "2", test = "F")## Anova Table (Type II tests)
##
## Response: slugs
## Sum Sq Df F value Pr(>F)
## Treatment 0.3003 1 0.8641 0.3684
## Mother_mass 0.0459 1 0.1322 0.7216
## Treatment:Mother_mass 0.6880 1 1.9794 0.1813
## Residuals 4.8660 14Live ~ Tmt * Mass
live_neo_mom_mass_lm <- lm(data = dab_clutch_mom,
live_neonates ~ Treatment * Mother_mass)
summary(live_neo_mom_mass_lm)##
## Call:
## lm(formula = live_neonates ~ Treatment * Mother_mass, data = dab_clutch_mom)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9782 -0.7241 0.0131 0.6161 3.9702
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.59933 2.63951 0.227 0.82366
## TreatmentW 6.85106 3.32457 2.061 0.05842 .
## Mother_mass 0.02592 0.00841 3.082 0.00811 **
## TreatmentW:Mother_mass -0.02411 0.01000 -2.410 0.03027 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.781 on 14 degrees of freedom
## Multiple R-squared: 0.4127, Adjusted R-squared: 0.2868
## F-statistic: 3.279 on 3 and 14 DF, p-value: 0.05271## Anova Table (Type II tests)
##
## Response: live_neonates
## Sum Sq Df F value Pr(>F)
## Treatment 3.150 1 0.9931 0.33590
## Mother_mass 12.064 1 3.8031 0.07147 .
## Treatment:Mother_mass 18.427 1 5.8090 0.03027 *
## Residuals 44.409 14
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## # A tibble: 2 × 6
## Treatment Mother_mass.trend std.error df statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 C 0.0259 0.00841 14 3.08 0.00811
## 2 W 0.00181 0.00542 14 0.335 0.743## contrast estimate SE df t.ratio p.value
## C - W 0.0241 0.01 14 2.410 0.0303## [1] 2.592295## [1] 0.1812418Plot
ggplot(
dab_clutch_mom,
aes(x = Mother_mass, y = live_neonates, color = Treatment)
) +
geom_point() +
geom_text(
aes(label = Mother_ID),
nudge_x = 0.25, nudge_y = 0.25,
check_overlap = T, show.legend = F
) +
geom_smooth(
method = "lm",
se = F,
formula = 'y~x'
)
## # A tibble: 9 × 6
## Mother_ID live_neonates dead_at_birth slugs total_embryos prop_live
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 133 13 1 2 16 0.812
## 2 126 10 1 0 11 0.909
## 3 125 8 2 0 10 0.8
## 4 128 8 2 0 10 0.8
## 5 129 8 0 1 9 0.889
## 6 134 8 0 1 9 0.889
## 7 105 6 1 1 8 0.75
## 8 117 5 2 1 8 0.625
## 9 131 5 0 1 6 0.833prop_live_mom_mass_lm <- lm(data = dab_clutch_mom,
prop_live ~ Treatment * Mother_mass)
summary(prop_live_mom_mass_lm)##
## Call:
## lm(formula = prop_live ~ Treatment * Mother_mass, data = dab_clutch_mom)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.14775 -0.09115 0.04526 0.06506 0.09820
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.850e-01 1.453e-01 6.092 2.78e-05 ***
## TreatmentW 1.954e-02 1.830e-01 0.107 0.916
## Mother_mass 7.043e-05 4.629e-04 0.152 0.881
## TreatmentW:Mother_mass 3.736e-05 5.506e-04 0.068 0.947
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09802 on 14 degrees of freedom
## Multiple R-squared: 0.05259, Adjusted R-squared: -0.1504
## F-statistic: 0.2591 on 3 and 14 DF, p-value: 0.8537## Anova Table (Type II tests)
##
## Response: prop_live
## Sum Sq Df F value Pr(>F)
## Treatment 0.004073 1 0.4239 0.5255
## Mother_mass 0.001435 1 0.1493 0.7050
## Treatment:Mother_mass 0.000044 1 0.0046 0.9469
## Residuals 0.134518 14Live ~ SVL
live_neo_mom_SVL_lm <- lm(data = dab_clutch_mom,
live_neonates ~ Mother_SVL)
summary(live_neo_mom_SVL_lm)##
## Call:
## lm(formula = live_neonates ~ Mother_SVL, data = dab_clutch_mom)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1395 -1.1640 0.0267 1.0432 3.4091
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.24127 5.95708 -0.880 0.392
## Mother_SVL 0.16625 0.07305 2.276 0.037 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.889 on 16 degrees of freedom
## Multiple R-squared: 0.2445, Adjusted R-squared: 0.1973
## F-statistic: 5.179 on 1 and 16 DF, p-value: 0.03696## Anova Table (Type II tests)
##
## Response: live_neonates
## Sum Sq Df F value Pr(>F)
## Mother_SVL 18.490 1 5.1792 0.03696 *
## Residuals 57.121 16
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1prop_live_mom_SVL_lm <- lm(data = dab_clutch_mom,
prop_live ~ Mother_SVL)
summary(prop_live_mom_SVL_lm)##
## Call:
## lm(formula = prop_live ~ Mother_SVL, data = dab_clutch_mom)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.16830 -0.08855 0.06075 0.07283 0.08828
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.060478 0.295104 3.594 0.00243 **
## Mother_SVL -0.001644 0.003619 -0.454 0.65579
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0936 on 16 degrees of freedom
## Multiple R-squared: 0.01273, Adjusted R-squared: -0.04897
## F-statistic: 0.2063 on 1 and 16 DF, p-value: 0.6558## Anova Table (Type II tests)
##
## Response: prop_live
## Sum Sq Df F value Pr(>F)
## Mother_SVL 0.001807 1 0.2063 0.6558
## Residuals 0.140178 16## # A tibble: 9 × 4
## # Groups: Treatment, slugs, dead_at_birth [9]
## Treatment slugs dead_at_birth n
## <fct> <dbl> <dbl> <int>
## 1 C 0 0 4
## 2 C 0 2 1
## 3 C 1 0 1
## 4 C 1 1 1
## 5 C 2 1 1
## 6 W 0 0 6
## 7 W 0 1 1
## 8 W 0 2 1
## 9 W 1 0 2DAB CEWL clutch ~ mother
this is what was done originally, but we now use all neonate data and do not avg by clutch
DAB_CEWL_LM_1 <- lm(data = dab_clutch_mom,
CEWL_g_m2h_mean ~
Treatment *
Mother_CEWL +
Tb_CEWL_c_mean +
msmt_temp_C_mean +
msmt_RH_percent_mean +
VPD_kPa_mean)
car::vif(DAB_CEWL_LM_1, type = "predictor")## GVIF Df GVIF^(1/(2*Df)) Interacts With
## Treatment 4.744030 3 1.296257 Mother_CEWL
## Mother_CEWL 4.744030 3 1.296257 Treatment
## Tb_CEWL_c_mean 1.590281 1 1.261064 --
## msmt_temp_C_mean 412.368805 1 20.306866 --
## msmt_RH_percent_mean 454.046656 1 21.308371 --
## VPD_kPa_mean 1121.024341 1 33.481702 --
## Other Predictors
## Treatment Tb_CEWL_c_mean, msmt_temp_C_mean, msmt_RH_percent_mean, VPD_kPa_mean
## Mother_CEWL Tb_CEWL_c_mean, msmt_temp_C_mean, msmt_RH_percent_mean, VPD_kPa_mean
## Tb_CEWL_c_mean Treatment, Mother_CEWL, msmt_temp_C_mean, msmt_RH_percent_mean, VPD_kPa_mean
## msmt_temp_C_mean Treatment, Mother_CEWL, Tb_CEWL_c_mean, msmt_RH_percent_mean, VPD_kPa_mean
## msmt_RH_percent_mean Treatment, Mother_CEWL, Tb_CEWL_c_mean, msmt_temp_C_mean, VPD_kPa_mean
## VPD_kPa_mean Treatment, Mother_CEWL, Tb_CEWL_c_mean, msmt_temp_C_mean, msmt_RH_percent_mean## Single term deletions
##
## Model:
## CEWL_g_m2h_mean ~ Treatment * Mother_CEWL + Tb_CEWL_c_mean +
## msmt_temp_C_mean + msmt_RH_percent_mean + VPD_kPa_mean
## Df Sum of Sq RSS AIC
## <none> 46.632 33.134
## Tb_CEWL_c_mean 1 1.5292 48.161 31.715
## msmt_temp_C_mean 1 5.5176 52.149 33.147
## msmt_RH_percent_mean 1 5.8505 52.482 33.262
## VPD_kPa_mean 1 5.7394 52.371 33.224
## Treatment:Mother_CEWL 1 3.5723 50.204 32.463## Anova Table (Type II tests)
##
## Response: CEWL_g_m2h_mean
## Sum Sq Df F value Pr(>F)
## Treatment 0.114 1 0.0244 0.8790
## Mother_CEWL 14.925 1 3.2007 0.1039
## Tb_CEWL_c_mean 1.529 1 0.3279 0.5795
## msmt_temp_C_mean 5.518 1 1.1832 0.3022
## msmt_RH_percent_mean 5.851 1 1.2546 0.2889
## VPD_kPa_mean 5.739 1 1.2308 0.2932
## Treatment:Mother_CEWL 3.572 1 0.7661 0.4020
## Residuals 46.632 10The first variable to drop from the model is humidity.
DAB_CEWL_LM_2 <- lm(data = dab_clutch_mom,
CEWL_g_m2h_mean ~
Treatment *
Mother_CEWL +
Tb_CEWL_c_mean +
msmt_temp_C_mean +
VPD_kPa_mean)
car::vif(DAB_CEWL_LM_2, type = "predictor")## GVIF Df GVIF^(1/(2*Df)) Interacts With
## Treatment 2.026678 3 1.124944 Mother_CEWL
## Mother_CEWL 2.026678 3 1.124944 Treatment
## Tb_CEWL_c_mean 1.232856 1 1.110341 --
## msmt_temp_C_mean 4.836938 1 2.199304 --
## VPD_kPa_mean 4.450099 1 2.109526 --
## Other Predictors
## Treatment Tb_CEWL_c_mean, msmt_temp_C_mean, VPD_kPa_mean
## Mother_CEWL Tb_CEWL_c_mean, msmt_temp_C_mean, VPD_kPa_mean
## Tb_CEWL_c_mean Treatment, Mother_CEWL, msmt_temp_C_mean, VPD_kPa_mean
## msmt_temp_C_mean Treatment, Mother_CEWL, Tb_CEWL_c_mean, VPD_kPa_mean
## VPD_kPa_mean Treatment, Mother_CEWL, Tb_CEWL_c_mean, msmt_temp_C_mean## Single term deletions
##
## Model:
## CEWL_g_m2h_mean ~ Treatment * Mother_CEWL + Tb_CEWL_c_mean +
## msmt_temp_C_mean + VPD_kPa_mean
## Df Sum of Sq RSS AIC
## <none> 52.482 33.262
## Tb_CEWL_c_mean 1 7.3271 59.809 33.614
## msmt_temp_C_mean 1 0.2636 52.746 31.352
## VPD_kPa_mean 1 0.0840 52.566 31.291
## Treatment:Mother_CEWL 1 9.8849 62.367 34.368## Anova Table (Type II tests)
##
## Response: CEWL_g_m2h_mean
## Sum Sq Df F value Pr(>F)
## Treatment 7.273 1 1.5243 0.24269
## Mother_CEWL 29.399 1 6.1619 0.03045 *
## Tb_CEWL_c_mean 7.327 1 1.5357 0.24105
## msmt_temp_C_mean 0.264 1 0.0553 0.81847
## VPD_kPa_mean 0.084 1 0.0176 0.89682
## Treatment:Mother_CEWL 9.885 1 2.0718 0.17789
## Residuals 52.482 11
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1DAB_CEWL_LM_3 <- lm(data = dab_clutch_mom,
CEWL_g_m2h_mean ~
Treatment *
Mother_CEWL +
Tb_CEWL_c_mean +
msmt_temp_C_mean)
drop1(DAB_CEWL_LM_3)## Single term deletions
##
## Model:
## CEWL_g_m2h_mean ~ Treatment * Mother_CEWL + Tb_CEWL_c_mean +
## msmt_temp_C_mean
## Df Sum of Sq RSS AIC
## <none> 52.566 31.291
## Tb_CEWL_c_mean 1 8.0957 60.662 31.869
## msmt_temp_C_mean 1 0.2507 52.817 29.376
## Treatment:Mother_CEWL 1 10.4629 63.029 32.558## Anova Table (Type II tests)
##
## Response: CEWL_g_m2h_mean
## Sum Sq Df F value Pr(>F)
## Treatment 7.365 1 1.6812 0.21914
## Mother_CEWL 35.111 1 8.0153 0.01514 *
## Tb_CEWL_c_mean 8.096 1 1.8481 0.19900
## msmt_temp_C_mean 0.251 1 0.0572 0.81496
## Treatment:Mother_CEWL 10.463 1 2.3885 0.14818
## Residuals 52.566 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1DAB_CEWL_LM_4 <- lm(data = dab_clutch_mom,
CEWL_g_m2h_mean ~
Treatment *
Mother_CEWL +
Tb_CEWL_c_mean)
drop1(DAB_CEWL_LM_4)## Single term deletions
##
## Model:
## CEWL_g_m2h_mean ~ Treatment * Mother_CEWL + Tb_CEWL_c_mean
## Df Sum of Sq RSS AIC
## <none> 52.817 29.376
## Tb_CEWL_c_mean 1 10.033 62.850 30.507
## Treatment:Mother_CEWL 1 11.926 64.743 31.041## Anova Table (Type II tests)
##
## Response: CEWL_g_m2h_mean
## Sum Sq Df F value Pr(>F)
## Treatment 9.769 1 2.4045 0.144985
## Mother_CEWL 41.815 1 10.2921 0.006859 **
## Tb_CEWL_c_mean 10.033 1 2.4694 0.140096
## Treatment:Mother_CEWL 11.926 1 2.9355 0.110382
## Residuals 52.817 13
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1DAB_CEWL_LM_5 <- lm(data = dab_clutch_mom,
CEWL_g_m2h_mean ~
Treatment +
Mother_CEWL +
Tb_CEWL_c_mean)
drop1(DAB_CEWL_LM_5)## Single term deletions
##
## Model:
## CEWL_g_m2h_mean ~ Treatment + Mother_CEWL + Tb_CEWL_c_mean
## Df Sum of Sq RSS AIC
## <none> 64.743 31.041
## Treatment 1 9.769 74.512 31.571
## Mother_CEWL 1 41.815 106.559 38.010
## Tb_CEWL_c_mean 1 14.512 79.256 32.682## Anova Table (Type II tests)
##
## Response: CEWL_g_m2h_mean
## Sum Sq Df F value Pr(>F)
## Treatment 9.769 1 2.1124 0.16815
## Mother_CEWL 41.815 1 9.0421 0.00942 **
## Tb_CEWL_c_mean 14.512 1 3.1381 0.09824 .
## Residuals 64.743 14
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1DAB_CEWL_LM_6 <- lm(data = dab_clutch_mom,
CEWL_g_m2h_mean ~
Mother_CEWL +
Tb_CEWL_c_mean)
drop1(DAB_CEWL_LM_6)## Single term deletions
##
## Model:
## CEWL_g_m2h_mean ~ Mother_CEWL + Tb_CEWL_c_mean
## Df Sum of Sq RSS AIC
## <none> 74.512 31.571
## Mother_CEWL 1 43.969 118.481 37.919
## Tb_CEWL_c_mean 1 15.255 89.767 32.923## Anova Table (Type II tests)
##
## Response: CEWL_g_m2h_mean
## Sum Sq Df F value Pr(>F)
## Mother_CEWL 43.969 1 8.8513 0.009439 **
## Tb_CEWL_c_mean 15.255 1 3.0709 0.100117
## Residuals 74.512 15
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1DAB_CEWL_LM_7 <- lm(data = dab_clutch_mom,
CEWL_g_m2h_mean ~
Mother_CEWL)
car::Anova(DAB_CEWL_LM_7, type = "2")## Anova Table (Type II tests)
##
## Response: CEWL_g_m2h_mean
## Sum Sq Df F value Pr(>F)
## Mother_CEWL 52.881 1 9.4255 0.007324 **
## Residuals 89.767 16
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Null model
Model Selection
clutch_cewl_models_all <- list(DAB_CEWL_LM_2,
DAB_CEWL_LM_3,
DAB_CEWL_LM_4,
DAB_CEWL_LM_5,
DAB_CEWL_LM_6,
DAB_CEWL_LM_7,
DAB_CEWL_LM_null)
#specify model names
clutch_cewl_mod_names_all <- c('model2', 'model3',
'model4', 'model5',
'model6', 'model7',
'modelnull')
#calculate AIC of each model
clutch_cewl_AICc_all <- data.frame(aictab(cand.set = clutch_cewl_models_all,
modnames = clutch_cewl_mod_names_all))
clutch_cewl_AICc_all## Modnames K AICc Delta_AICc ModelLik AICcWt LL
## 6 model7 3 87.71926 0.00000000 1.0000000000 0.3469297055 -40.00249
## 5 model6 4 87.72938 0.01011992 0.9949528202 0.3451786889 -38.32623
## 4 model5 5 89.12286 1.40359817 0.4956927089 0.1719705255 -37.06143
## 3 model4 6 90.09444 2.37517755 0.3049556953 0.1057981896 -35.22904
## 7 modelnull 2 93.14190 5.42264649 0.0664488206 0.0230530698 -44.17095
## 2 model3 7 95.57243 7.85316762 0.0197108938 0.0068382946 -35.18621
## 1 model2 8 102.34363 14.62436885 0.0006673577 0.0002315262 -35.17181
## Cum.Wt
## 6 0.3469297
## 5 0.6921084
## 4 0.8640789
## 3 0.9698771
## 7 0.9929302
## 2 0.9997685
## 1 1.0000000So the top three equivalent models are 7, 6, & 5. 6/7 are almost exactly the same, with 5 trailing a bit behind.
Model 7 is clutch avg CEWL ~ mother CEWL. Model 6 adds clutch average body temperature at the time of measurement. Model 5 adds Tb and mother’s treatment. However, Tb and mother’s treatment did not have significant effects.
Final Model
DAB_CEWL_LM_BEST <- lm(data = dab_clutch_mom,
CEWL_g_m2h_mean ~
Mother_CEWL)
car::Anova(DAB_CEWL_LM_BEST, type = "2")## Anova Table (Type II tests)
##
## Response: CEWL_g_m2h_mean
## Sum Sq Df F value Pr(>F)
## Mother_CEWL 52.881 1 9.4255 0.007324 **
## Residuals 89.767 16
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Call:
## lm(formula = CEWL_g_m2h_mean ~ Mother_CEWL, data = dab_clutch_mom)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8832 -1.8846 0.0906 1.6421 4.8088
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.4366 2.5501 1.74 0.10109
## Mother_CEWL 0.3975 0.1295 3.07 0.00732 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.369 on 16 degrees of freedom
## Multiple R-squared: 0.3707, Adjusted R-squared: 0.3314
## F-statistic: 9.425 on 1 and 16 DF, p-value: 0.007324
Check Tb
##
## Call:
## lm(formula = Tb_CEWL_c ~ msmt_temp_C, data = dab_all)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0528 -0.7894 -0.1074 0.7566 2.9472
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.8366 3.9149 4.811 7.18e-06 ***
## msmt_temp_C 0.3643 0.1518 2.400 0.0188 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.21 on 78 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.06876, Adjusted R-squared: 0.05682
## F-statistic: 5.759 on 1 and 78 DF, p-value: 0.01879DAB_Tb <- lmerTest::lmer(data = dab_all,
Tb_CEWL_c ~
Treatment +
(1|Mother_ID))
anova(DAB_Tb, test = "F", type = "2")## Type II Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Treatment 0.045615 0.045615 1 16.51 0.0418 0.8406## $emmeans
## Treatment emmean SE df lower.CL upper.CL
## C 28.3 0.312 16.3 27.6 28.9
## W 28.2 0.276 15.7 27.6 28.8
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## C - W 0.0851 0.416 16 0.204 0.8407
##
## Degrees-of-freedom method: kenward-rogerCEWL ~ osml
##
## Call:
## lm(formula = CEWL_g_m2h ~ Plasma_Osmol_Rep_mean, data = dab_all)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.1094 -2.1594 -0.2557 1.6502 12.0127
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.86750 8.20803 -0.471 0.6388
## Plasma_Osmol_Rep_mean 0.05364 0.02772 1.935 0.0566 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.814 on 78 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.04581, Adjusted R-squared: 0.03358
## F-statistic: 3.745 on 1 and 78 DF, p-value: 0.05659##
## Call:
## lm(formula = CEWL_g_m2h ~ Plasma_Osmol_Rep_mean, data = ps_all)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0605 -2.0276 -0.0202 2.8676 5.3066
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.55677 4.82017 2.190 0.0357 *
## Plasma_Osmol_Rep_mean 0.01723 0.01514 1.138 0.2632
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.956 on 33 degrees of freedom
## Multiple R-squared: 0.03778, Adjusted R-squared: 0.008624
## F-statistic: 1.296 on 1 and 33 DF, p-value: 0.2632DAB CEWL DIFFS
DAB_CEWL_LMM_0 <- lme4::lmer(data = dab_all,
CEWL_g_m2h ~
Treatment *
Mother_CEWL +
Tb_CEWL_c +
msmt_temp_C +
msmt_RH_percent +
VPD_kPa +
(1|Mother_ID))
car::vif(DAB_CEWL_LMM_0)## Treatment Mother_CEWL Tb_CEWL_c
## 42.504746 5.885250 1.115189
## msmt_temp_C msmt_RH_percent VPD_kPa
## 254.801919 288.145293 691.560970
## Treatment:Mother_CEWL
## 41.228742## Single term deletions
##
## Model:
## CEWL_g_m2h ~ Treatment * Mother_CEWL + Tb_CEWL_c + msmt_temp_C +
## msmt_RH_percent + VPD_kPa + (1 | Mother_ID)
## npar AIC
## <none> 416.55
## Tb_CEWL_c 1 416.73
## msmt_temp_C 1 417.66
## msmt_RH_percent 1 418.07
## VPD_kPa 1 417.94
## Treatment:Mother_CEWL 1 415.76## Analysis of Variance Table
## npar Sum Sq Mean Sq F value
## Treatment 1 26.583 26.583 3.4268
## Mother_CEWL 1 102.933 102.933 13.2691
## Tb_CEWL_c 1 36.748 36.748 4.7372
## msmt_temp_C 1 12.596 12.596 1.6237
## msmt_RH_percent 1 0.320 0.320 0.0413
## VPD_kPa 1 33.636 33.636 4.3361
## Treatment:Mother_CEWL 1 6.788 6.788 0.8750DAB_CEWL_LMM_1 <- lme4::lmer(data = dab_all,
CEWL_g_m2h ~
Treatment +
Mother_CEWL +
Tb_CEWL_c +
msmt_temp_C +
msmt_RH_percent +
VPD_kPa +
(1|Mother_ID))
DAB_CEWL_LMM_11 <- lme4::lmer(data = dab_all,
CEWL_g_m2h ~
Treatment *
Mother_CEWL +
Tb_CEWL_c +
msmt_temp_C +
VPD_kPa +
(1|Mother_ID))
car::vif(DAB_CEWL_LMM_1)## Treatment Mother_CEWL Tb_CEWL_c msmt_temp_C msmt_RH_percent
## 1.467280 1.483813 1.116464 221.687480 251.824614
## VPD_kPa
## 595.768696## Treatment Mother_CEWL Tb_CEWL_c
## 33.946352 5.530680 1.052778
## msmt_temp_C VPD_kPa Treatment:Mother_CEWL
## 3.798479 3.643235 35.785122## Single term deletions
##
## Model:
## CEWL_g_m2h ~ Treatment * Mother_CEWL + Tb_CEWL_c + msmt_temp_C +
## VPD_kPa + (1 | Mother_ID)
## npar AIC
## <none> 418.07
## Tb_CEWL_c 1 419.69
## msmt_temp_C 1 416.81
## VPD_kPa 1 416.29
## Treatment:Mother_CEWL 1 419.24start good models
(good = no collinearity)
DAB_CEWL_LMM_1a <- lme4::lmer(data = dab_all,
CEWL_g_m2h ~
Treatment + Mother_CEWL +
Tb_CEWL_c +
msmt_temp_C + # remove RH to fix multicollinearity
VPD_kPa +
(1|Mother_ID))
car::vif(DAB_CEWL_LMM_1a)## Treatment Mother_CEWL Tb_CEWL_c msmt_temp_C VPD_kPa
## 1.047027 1.409371 1.044307 3.726919 3.355360## Single term deletions
##
## Model:
## CEWL_g_m2h ~ Treatment + Mother_CEWL + Tb_CEWL_c + msmt_temp_C +
## VPD_kPa + (1 | Mother_ID)
## npar AIC
## <none> 419.24
## Treatment 1 419.01
## Mother_CEWL 1 423.16
## Tb_CEWL_c 1 420.97
## msmt_temp_C 1 417.77
## VPD_kPa 1 417.24## Analysis of Variance Table
## npar Sum Sq Mean Sq F value
## Treatment 1 21.414 21.414 2.7518
## Mother_CEWL 1 82.634 82.634 10.6187
## Tb_CEWL_c 1 32.242 32.242 4.1432
## msmt_temp_C 1 11.823 11.823 1.5193
## VPD_kPa 1 0.089 0.089 0.0115DAB_CEWL_LMM_2 <- lme4::lmer(data = dab_all,
CEWL_g_m2h ~
Treatment + Mother_CEWL +
Tb_CEWL_c +
msmt_temp_C +
(1|Mother_ID))
DAB_CEWL_LMM_2v <- lme4::lmer(data = dab_all,
CEWL_g_m2h ~
Treatment + Mother_CEWL +
Tb_CEWL_c +
VPD_kPa +
(1|Mother_ID))
summary(DAB_CEWL_LMM_2v)## Linear mixed model fit by REML ['lmerMod']
## Formula: CEWL_g_m2h ~ Treatment + Mother_CEWL + Tb_CEWL_c + VPD_kPa +
## (1 | Mother_ID)
## Data: dab_all
##
## REML criterion at convergence: 400.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3257 -0.5481 -0.1215 0.3664 3.1862
##
## Random effects:
## Groups Name Variance Std.Dev.
## Mother_ID (Intercept) 2.889 1.700
## Residual 7.857 2.803
## Number of obs: 80, groups: Mother_ID, 18
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) -17.5678 10.3296 -1.701
## TreatmentW -1.3391 1.0469 -1.279
## Mother_CEWL 0.3917 0.1192 3.285
## Tb_CEWL_c 0.5949 0.2987 1.991
## VPD_kPa 3.0621 3.1028 0.987
##
## Correlation of Fixed Effects:
## (Intr) TrtmnW M_CEWL T_CEWL
## TreatmentW -0.181
## Mother_CEWL -0.207 0.075
## Tb_CEWL_c -0.749 0.001 -0.088
## VPD_kPa -0.556 0.179 0.090 -0.081## Single term deletions
##
## Model:
## CEWL_g_m2h ~ Treatment + Mother_CEWL + Tb_CEWL_c + msmt_temp_C +
## (1 | Mother_ID)
## npar AIC
## <none> 417.24
## Treatment 1 417.01
## Mother_CEWL 1 422.89
## Tb_CEWL_c 1 419.03
## msmt_temp_C 1 416.92## Analysis of Variance Table
## npar Sum Sq Mean Sq F value
## Treatment 1 22.616 22.616 2.9173
## Mother_CEWL 1 87.344 87.344 11.2667
## Tb_CEWL_c 1 33.280 33.280 4.2929
## msmt_temp_C 1 12.006 12.006 1.5487DAB_CEWL_LMM_3 <- lme4::lmer(data = dab_all,
CEWL_g_m2h ~
Treatment + Mother_CEWL +
Tb_CEWL_c +
(1|Mother_ID))
DAB_CEWL_LMM_3int <- lme4::lmer(data = dab_all,
CEWL_g_m2h ~
Treatment * Mother_CEWL +
Tb_CEWL_c +
(1|Mother_ID))
summary(DAB_CEWL_LMM_3)## Linear mixed model fit by REML ['lmerMod']
## Formula: CEWL_g_m2h ~ Treatment + Mother_CEWL + Tb_CEWL_c + (1 | Mother_ID)
## Data: dab_all
##
## REML criterion at convergence: 405.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3067 -0.5956 -0.1142 0.3314 3.2341
##
## Random effects:
## Groups Name Variance Std.Dev.
## Mother_ID (Intercept) 2.846 1.687
## Residual 7.870 2.805
## Number of obs: 80, groups: Mother_ID, 18
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) -11.9346 8.5779 -1.391
## TreatmentW -1.5239 1.0257 -1.486
## Mother_CEWL 0.3812 0.1182 3.224
## Tb_CEWL_c 0.6201 0.2977 2.083
##
## Correlation of Fixed Effects:
## (Intr) TrtmnW M_CEWL
## TreatmentW -0.099
## Mother_CEWL -0.189 0.060
## Tb_CEWL_c -0.959 0.016 -0.082## Single term deletions
##
## Model:
## CEWL_g_m2h ~ Treatment + Mother_CEWL + Tb_CEWL_c + (1 | Mother_ID)
## npar AIC
## <none> 416.92
## Treatment 1 417.44
## Mother_CEWL 1 424.45
## Tb_CEWL_c 1 419.59## Analysis of Variance Table
## npar Sum Sq Mean Sq F value
## Treatment 1 23.621 23.621 3.0013
## Mother_CEWL 1 91.286 91.286 11.5989
## Tb_CEWL_c 1 34.153 34.153 4.3395DAB_CEWL_LMM_4 <- lme4::lmer(data = dab_all,
CEWL_g_m2h ~
Mother_CEWL +
Tb_CEWL_c +
(1|Mother_ID))
summary(DAB_CEWL_LMM_4)## Linear mixed model fit by REML ['lmerMod']
## Formula: CEWL_g_m2h ~ Mother_CEWL + Tb_CEWL_c + (1 | Mother_ID)
## Data: dab_all
##
## REML criterion at convergence: 410
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4120 -0.5772 -0.1474 0.4340 3.2200
##
## Random effects:
## Groups Name Variance Std.Dev.
## Mother_ID (Intercept) 3.205 1.790
## Residual 7.869 2.805
## Number of obs: 80, groups: Mother_ID, 18
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) -12.9081 8.6252 -1.497
## Mother_CEWL 0.3913 0.1225 3.194
## Tb_CEWL_c 0.6173 0.3000 2.058
##
## Correlation of Fixed Effects:
## (Intr) M_CEWL
## Mother_CEWL -0.195
## Tb_CEWL_c -0.960 -0.080## Single term deletions
##
## Model:
## CEWL_g_m2h ~ Mother_CEWL + Tb_CEWL_c + (1 | Mother_ID)
## npar AIC
## <none> 417.44
## Mother_CEWL 1 424.34
## Tb_CEWL_c 1 419.81## Analysis of Variance Table
## npar Sum Sq Mean Sq F value
## Mother_CEWL 1 89.300 89.300 11.3486
## Tb_CEWL_c 1 33.325 33.325 4.2351DAB_CEWL_LMM_5 <- lme4::lmer(data = dab_all,
CEWL_g_m2h ~
Mother_CEWL +
(1|Mother_ID))
summary(DAB_CEWL_LMM_5)## Linear mixed model fit by REML ['lmerMod']
## Formula: CEWL_g_m2h ~ Mother_CEWL + (1 | Mother_ID)
## Data: dab_all
##
## REML criterion at convergence: 413.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7448 -0.5548 -0.1630 0.4702 3.1358
##
## Random effects:
## Groups Name Variance Std.Dev.
## Mother_ID (Intercept) 3.920 1.980
## Residual 7.978 2.825
## Number of obs: 80, groups: Mother_ID, 18
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 4.1727 2.5782 1.618
## Mother_CEWL 0.4097 0.1308 3.131
##
## Correlation of Fixed Effects:
## (Intr)
## Mother_CEWL -0.976## Analysis of Variance Table
## npar Sum Sq Mean Sq F value
## Mother_CEWL 1 78.209 78.209 9.8027Model Selection
cewl_models_all <- list(DAB_CEWL_LMM_1a, DAB_CEWL_LMM_2, DAB_CEWL_LMM_2v,
DAB_CEWL_LMM_3, DAB_CEWL_LMM_4, DAB_CEWL_LMM_5,
DAB_CEWL_LMM_NULL)
#specify model names
cewl_mod_names_all <- c('model1',
'model2', 'model2 VPD',
'model3',
'model4',
'model5',
'modelnull')
#calculate AIC of each model
cewl_AICc_all <- data.frame(aictab(cand.set = cewl_models_all,
modnames = cewl_mod_names_all))
cewl_AICc_all## Modnames K AICc Delta_AICc ModelLik AICcWt Res.LL Cum.Wt
## 1 model1 8 416.2621 0.000000 1.000000000 0.385961219 -199.1170 0.3859612
## 3 model2 VPD 7 416.4001 0.137955 0.933347685 0.360236010 -200.4223 0.7461972
## 4 model3 6 419.0710 2.808868 0.245505920 0.094755764 -202.9602 0.8409530
## 2 model2 7 419.1056 2.843511 0.241290072 0.093128610 -201.7750 0.9340816
## 5 model4 5 420.7823 4.520133 0.104343549 0.040272563 -204.9857 0.9743542
## 6 model5 4 422.0008 5.738702 0.056735748 0.021897799 -206.7338 0.9962520
## 7 modelnull 3 425.5311 9.269010 0.009710911 0.003748035 -209.6077 1.0000000Okay! after some additional investigations, it seems like the model needs to include VPD.
DAB_CEWL_LMM_2v <- lme4::lmer(data = dab_all, CEWL_g_m2h ~ Treatment + Mother_CEWL + Tb_CEWL_c + VPD_kPa + (1|Mother_ID))
Final Model + emmeans
DAB_CEWL_LMM_BEST_simple <- lmerTest::lmer(data = dab_all,
CEWL_g_m2h ~
Treatment +
Mother_CEWL +
Tb_CEWL_c +
VPD_kPa +
(1|Mother_ID))
summary(DAB_CEWL_LMM_BEST_simple)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CEWL_g_m2h ~ Treatment + Mother_CEWL + Tb_CEWL_c + VPD_kPa +
## (1 | Mother_ID)
## Data: dab_all
##
## REML criterion at convergence: 400.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3257 -0.5481 -0.1215 0.3664 3.1862
##
## Random effects:
## Groups Name Variance Std.Dev.
## Mother_ID (Intercept) 2.889 1.700
## Residual 7.857 2.803
## Number of obs: 80, groups: Mother_ID, 18
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -17.5678 10.3296 43.4663 -1.701 0.09614 .
## TreatmentW -1.3391 1.0469 14.2913 -1.279 0.22125
## Mother_CEWL 0.3917 0.1192 14.4439 3.285 0.00523 **
## Tb_CEWL_c 0.5949 0.2987 74.7079 1.991 0.05009 .
## VPD_kPa 3.0621 3.1028 21.2580 0.987 0.33480
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) TrtmnW M_CEWL T_CEWL
## TreatmentW -0.181
## Mother_CEWL -0.207 0.075
## Tb_CEWL_c -0.749 0.001 -0.088
## VPD_kPa -0.556 0.179 0.090 -0.081## Type II Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Treatment 12.855 12.855 1 14.291 1.6361 0.221245
## Mother_CEWL 84.809 84.809 1 14.444 10.7935 0.005227 **
## Tb_CEWL_c 31.160 31.160 1 74.708 3.9657 0.050093 .
## VPD_kPa 7.653 7.653 1 21.258 0.9739 0.334802
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## R2m R2c
## [1,] 0.3075859 0.4937201## $emmeans
## Treatment emmean SE df lower.CL upper.CL
## C 12.8 0.784 13.9 11.10 14.5
## W 11.4 0.683 13.3 9.97 12.9
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## C - W 1.34 1.05 13.7 1.277 0.2228
##
## Degrees-of-freedom method: kenward-rogerDAB_CEWL_emmeans <- emmeans(DAB_CEWL_LMM_BEST_simple, "Treatment")
DAB_CEWL_emmeans_CI <- tidy(confint(DAB_CEWL_emmeans))DAB_CEWL_LMM_BEST_full <- lmerTest::lmer(data = dab_all,
CEWL_g_m2h ~
Treatment +
Mother_CEWL +
Tb_CEWL_c +
msmt_temp_C +
VPD_kPa +
(1|Mother_ID))
summary(DAB_CEWL_LMM_BEST_full)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CEWL_g_m2h ~ Treatment + Mother_CEWL + Tb_CEWL_c + msmt_temp_C +
## VPD_kPa + (1 | Mother_ID)
## Data: dab_all
##
## REML criterion at convergence: 398.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3517 -0.5491 -0.1371 0.3740 3.1195
##
## Random effects:
## Groups Name Variance Std.Dev.
## Mother_ID (Intercept) 3.263 1.806
## Residual 7.782 2.790
## Number of obs: 80, groups: Mother_ID, 18
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -30.0691 19.4637 21.4560 -1.545 0.1370
## TreatmentW -1.2540 1.0912 13.4906 -1.149 0.2704
## Mother_CEWL 0.3317 0.1455 16.1331 2.280 0.0366 *
## Tb_CEWL_c 0.5450 0.3045 73.9954 1.790 0.0776 .
## msmt_temp_C 0.8627 1.1108 23.6049 0.777 0.4451
## VPD_kPa -0.6125 5.7224 26.0644 -0.107 0.9156
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) TrtmnW M_CEWL T_CEWL msm__C
## TreatmentW -0.180
## Mother_CEWL 0.345 0.012
## Tb_CEWL_c -0.247 -0.016 0.022
## msmt_temp_C -0.842 0.097 -0.527 -0.175
## VPD_kPa 0.528 0.019 0.480 0.103 -0.830## R2m R2c
## [1,] 0.3118644 0.515141## Type II Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Treatment 10.277 10.277 1 13.491 1.3207 0.27044
## Mother_CEWL 40.439 40.439 1 16.133 5.1965 0.03656 *
## Tb_CEWL_c 24.929 24.929 1 73.995 3.2034 0.07758 .
## msmt_temp_C 4.693 4.693 1 23.605 0.6031 0.44512
## VPD_kPa 0.089 0.089 1 26.064 0.0115 0.91558
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Model Assumptions
##
## Shapiro-Wilk normality test
##
## data: residuals(DAB_CEWL_LMM_BEST_simple)
## W = 0.8914, p-value = 5.377e-06looks fine, but not great
Partial Regression
mother_effects_data <- summary(effect("Mother_CEWL",
DAB_CEWL_LMM_BEST_simple))
mother_effects_df <- data_frame(
meanval = mother_effects_data$effect,
lowCI = mother_effects_data$lower,
highCI = mother_effects_data$upper
) %>%
mutate(Mother_CEWL = c(10, 15, 19, 23, 28))
mother_effects_df## # A tibble: 5 × 4
## meanval lowCI highCI Mother_CEWL
## <dbl[1d]> <dbl[1d]> <dbl[1d]> <dbl>
## 1 8.39 5.97 10.8 10
## 2 10.3 8.92 11.8 15
## 3 11.9 10.9 12.9 19
## 4 13.5 12.1 14.8 23
## 5 15.4 13.1 17.8 28Tb_effects_data <- summary(effect("Tb_CEWL_c",
DAB_CEWL_LMM_BEST_simple))
Tb_effects_df <- data_frame(
meanval = Tb_effects_data$effect,
lowCI = Tb_effects_data$lower,
highCI = Tb_effects_data$upper
) %>%
mutate(Tb_CEWL_c = c(26, 27, 29, 30, 31))
Tb_effects_df## # A tibble: 5 × 4
## meanval lowCI highCI Tb_CEWL_c
## <dbl[1d]> <dbl[1d]> <dbl[1d]> <dbl>
## 1 10.7 9.02 12.4 26
## 2 11.3 10.0 12.5 27
## 3 12.5 11.4 13.6 29
## 4 13.1 11.6 14.5 30
## 5 13.7 11.7 15.6 31VPD_effects_data <- summary(effect("VPD_kPa",
DAB_CEWL_LMM_BEST_simple))
VPD_effects_df <- data_frame(
meanval = VPD_effects_data$effect,
lowCI = VPD_effects_data$lower,
highCI = VPD_effects_data$upper
) %>%
mutate(VPD_kPa = c(1.6, 1.8, 1.9, 2.1, 2.3))
VPD_effects_df## # A tibble: 5 × 4
## meanval lowCI highCI VPD_kPa
## <dbl[1d]> <dbl[1d]> <dbl[1d]> <dbl>
## 1 10.9 8.38 13.4 1.6
## 2 11.5 10.0 13.0 1.8
## 3 11.8 10.7 12.9 1.9
## 4 12.4 11.1 13.7 2.1
## 5 13.0 10.8 15.3 2.3Check PS
PS_CEWL_LMM_mother_simp <- lmerTest::lmer(data = ps_all_mother,
CEWL_g_m2h ~
Mother_CEWL +
(1|Mother_ID))
summary(PS_CEWL_LMM_mother_simp)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CEWL_g_m2h ~ Mother_CEWL + (1 | Mother_ID)
## Data: ps_all_mother
##
## REML criterion at convergence: 175.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.75703 -0.72379 0.01135 0.73227 1.97394
##
## Random effects:
## Groups Name Variance Std.Dev.
## Mother_ID (Intercept) 0.000 0.000
## Residual 9.024 3.004
## Number of obs: 35, groups: Mother_ID, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 17.34677 2.91566 33.00000 5.950 1.12e-06 ***
## Mother_CEWL -0.06766 0.14575 33.00000 -0.464 0.646
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Mother_CEWL -0.985
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')## Type II Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Mother_CEWL 1.9443 1.9443 1 33 0.2155 0.6456PS_CEWL_LMM_mother <- lmerTest::lmer(data = ps_all_mother,
CEWL_g_m2h ~
Treatment +
Mother_CEWL +
Tb_CEWL_c +
VPD_kPa +
(1|Mother_ID))
summary(PS_CEWL_LMM_mother)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CEWL_g_m2h ~ Treatment + Mother_CEWL + Tb_CEWL_c + VPD_kPa +
## (1 | Mother_ID)
## Data: ps_all_mother
##
## REML criterion at convergence: 166.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.52396 -0.68095 -0.05939 0.69543 1.77997
##
## Random effects:
## Groups Name Variance Std.Dev.
## Mother_ID (Intercept) 0.000 0.000
## Residual 9.566 3.093
## Number of obs: 35, groups: Mother_ID, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 15.22859 21.21809 30.00000 0.718 0.478
## TreatmentW -1.14817 1.19906 30.00000 -0.958 0.346
## Mother_CEWL -0.10936 0.16498 30.00000 -0.663 0.512
## Tb_CEWL_c -0.01035 0.53759 30.00000 -0.019 0.985
## VPD_kPa 1.87699 6.70663 30.00000 0.280 0.781
##
## Correlation of Fixed Effects:
## (Intr) TrtmnW M_CEWL T_CEWL
## TreatmentW -0.339
## Mother_CEWL -0.293 0.279
## Tb_CEWL_c -0.722 0.374 -0.046
## VPD_kPa -0.667 -0.001 0.263 -0.010
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')## Type II Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Treatment 8.7715 8.7715 1 30 0.9169 0.3459
## Mother_CEWL 4.2030 4.2030 1 30 0.4394 0.5125
## Tb_CEWL_c 0.0035 0.0035 1 30 0.0004 0.9848
## VPD_kPa 0.7493 0.7493 1 30 0.0783 0.7815## R2m R2c
## [1,] 0.03773164 0.03773164## $emmeans
## Treatment emmean SE df lower.CL upper.CL
## C 16.5 0.770 3.10 14.1 18.9
## W 15.4 0.865 3.36 12.8 18.0
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## C - W 1.15 1.24 3.26 0.924 0.4189
##
## Degrees-of-freedom method: kenward-rogerPS_CEWL_LMM_mother2 <- lme4::lmer(data = ps_all_mother,
CEWL_g_m2h ~
Treatment +
Mother_CEWL +
Tb_CEWL_c +
VPD_kPa +
(1|Mother_ID))
drop1(PS_CEWL_LMM_mother2)## Single term deletions
##
## Model:
## CEWL_g_m2h ~ Treatment + Mother_CEWL + Tb_CEWL_c + VPD_kPa +
## (1 | Mother_ID)
## npar AIC
## <none> 186.97
## Treatment 1 186.02
## Mother_CEWL 1 185.48
## Tb_CEWL_c 1 184.97
## VPD_kPa 1 185.06PS_CEWL_LMM_mother3 <- lme4::lmer(data = ps_all_mother,
CEWL_g_m2h ~
Treatment +
Mother_CEWL +
VPD_kPa +
(1|Mother_ID))
drop1(PS_CEWL_LMM_mother3)## Single term deletions
##
## Model:
## CEWL_g_m2h ~ Treatment + Mother_CEWL + VPD_kPa + (1 | Mother_ID)
## npar AIC
## <none> 184.97
## Treatment 1 184.17
## Mother_CEWL 1 183.48
## VPD_kPa 1 183.06## Analysis of Variance Table
## npar Sum Sq Mean Sq F value
## Treatment 1 6.4075 6.4075 0.6921
## Mother_CEWL 1 5.5940 5.5940 0.6043
## VPD_kPa 1 0.7484 0.7484 0.0808PUB FIG
CEWL CLUTCH ~ MOTHER
ggplot(
dab_clutch_mom,
aes(
x = Mother_CEWL,
y = CEWL_g_m2h_mean
)
) +
geom_abline(
slope = 1, intercept = 0,
linetype = "dashed", color = "darkgrey"
) +
geom_pointrange(
aes(ymin = CEWL_g_m2h_mean-CEWL_g_m2h_sd,
ymax = CEWL_g_m2h_mean+CEWL_g_m2h_sd,
color = Treatment, shape = Treatment),
#color = "springgreen3",
alpha = 0.6, size = 0.2, linewidth = 0.3
) +
geom_point(aes(color = Treatment, shape = Treatment)) +
geom_smooth(
se = F,
formula = 'y~x',
method = "lm",
color = "black",
linewidth = 1
) +
scale_x_continuous(
limits = c(0, 30),
breaks = c(0, 10, 20, 30),
name = expression('Mother CEWL (g '*m^-2*' '*h^-1*')')
) +
scale_y_continuous(
limits = c(0, 21),
breaks = c(0, 10, 20),
name = expression('Neonate CEWL (g '*m^-2*' '*h^-1*')')
) -> neo_mom_CEWL
neo_mom_CEWL
partreg CEWL~MOM
ggplot() +
geom_abline(
slope = 1, intercept = 0,
linetype = "dashed", color = "darkgrey"
) +
geom_pointrange(
data = dab_clutch_mom,
aes(
x = Mother_CEWL,
y = CEWL_g_m2h_mean,
ymin = CEWL_g_m2h_mean-CEWL_g_m2h_sd,
ymax = CEWL_g_m2h_mean+CEWL_g_m2h_sd,
color = Treatment, shape = Treatment),
alpha = 0.6, size = 0.2, linewidth = 0.3, show.legend = FALSE
) +
geom_point(
data = dab_clutch_mom,
aes(
x = Mother_CEWL,
y = CEWL_g_m2h_mean,
color = Treatment, shape = Treatment
)) +
geom_ribbon(
data = mother_effects_df,
aes(x = Mother_CEWL, ymin = lowCI, ymax = highCI),
alpha = 0.2
) +
geom_line(
data = mother_effects_df,
aes(x = Mother_CEWL, y = meanval),
color = "black",
linewidth = 1
) +
# prettys
scale_color_manual(
values = c("darkgoldenrod2", "royalblue4"),
labels = c("C" = "Control", "W" = "Hydration"),
name = NULL
) +
scale_shape_manual(
values = c(15, 17),
labels = c("C" = "Control", "W" = "Hydration"),
name = NULL
) +
theme(
legend.position = "inside",
legend.position.inside = c(0.75, 0.15),
legend.background = element_blank()
) +
# annotate(
# geom = "text", label = expression(''*R^2*'=0.5'),
# x = 2, y = 13, size = 3
# ) +
scale_x_continuous(
limits = c(0, 30),
breaks = c(0, 10, 20, 30),
name = expression('Mother CEWL (g '*m^-2*' '*h^-1*')')
) +
scale_y_continuous(
limits = c(0, 21),
breaks = c(0, 10, 20),
name = expression('Neonate CEWL (g '*m^-2*' '*h^-1*')')
) -> neo_mom_CEWL_partreg
neo_mom_CEWL_partreg
export it:
ggsave(
filename = "Fig4_neonate_mother_CEWL.pdf",
plot = neo_mom_CEWL_partreg,
path = "./results_figures",
device = "pdf",
dpi = 600,
units = "mm",
width = 150,
height = 100
)
ggsave(
filename = "Fig4_neonate_mother_CEWL.tiff",
plot = neo_mom_CEWL_partreg,
path = "./results_figures",
device = "tiff",
dpi = 600,
units = "mm",
width = 150,
height = 100
)partreg CEWL~Tb
ggplot() +
geom_pointrange(
data = dab_clutch_mom,
aes(
x = Tb_CEWL_c_mean,
y = CEWL_g_m2h_mean,
ymin = CEWL_g_m2h_mean-CEWL_g_m2h_sd,
ymax = CEWL_g_m2h_mean+CEWL_g_m2h_sd,
color = Treatment, shape = Treatment),
alpha = 0.6, size = 0.2, linewidth = 0.3
) +
geom_point(
data = dab_clutch_mom,
aes(
x = Tb_CEWL_c_mean,
y = CEWL_g_m2h_mean,
color = Treatment, shape = Treatment
)) +
geom_ribbon(
data = Tb_effects_df,
aes(x = Tb_CEWL_c, ymin = lowCI, ymax = highCI),
alpha = 0.2
) +
geom_line(
data = Tb_effects_df,
aes(x = Tb_CEWL_c, y = meanval),
color = "black",
linewidth = 1
) +
# prettys
scale_color_manual(
values = c("darkgoldenrod2", "royalblue4"),
labels = c("C" = "Control", "W" = "Hydration"),
name = NULL
) +
scale_shape_manual(
values = c(15, 17),
labels = c("C" = "Control", "W" = "Hydration"),
name = NULL
) +
theme(
legend.position = "inside",
legend.position.inside = c(0.1, 0.1),
legend.background = element_blank()
) +
# scale_x_continuous(
# limits = c(26, 30),
# breaks = c(0, 10, 20, 30),
# name = expression('Mother CEWL (g '*m^-2*' '*h^-1*')')
# ) +
scale_y_continuous(
#limits = c(0, 21),
breaks = c(0, 10, 20),
name = expression('Neonate CEWL (g '*m^-2*' '*h^-1*')')
) -> neo_Tb_CEWL_partreg
neo_Tb_CEWL_partreg
partreg CEWL~VPD
ggplot() +
geom_pointrange(
data = dab_clutch_mom,
aes(
x = VPD_kPa_mean,
y = CEWL_g_m2h_mean,
ymin = CEWL_g_m2h_mean-CEWL_g_m2h_sd,
ymax = CEWL_g_m2h_mean+CEWL_g_m2h_sd,
color = Treatment, shape = Treatment),
alpha = 0.6, size = 0.2, linewidth = 0.3
) +
geom_point(
data = dab_clutch_mom,
aes(
x = VPD_kPa_mean,
y = CEWL_g_m2h_mean,
color = Treatment, shape = Treatment
)) +
geom_ribbon(
data = VPD_effects_df,
aes(x = VPD_kPa, ymin = lowCI, ymax = highCI),
alpha = 0.2
) +
geom_line(
data = VPD_effects_df,
aes(x = VPD_kPa, y = meanval),
color = "black",
linewidth = 1
) +
# prettys
scale_color_manual(
values = c("darkgoldenrod2", "royalblue4"),
labels = c("C" = "Control", "W" = "Hydration"),
name = NULL
) +
scale_shape_manual(
values = c(15, 17),
labels = c("C" = "Control", "W" = "Hydration"),
name = NULL
) +
theme(
legend.position = "inside",
legend.position.inside = c(0.1, 0.1),
legend.background = element_blank()
) +
# scale_x_continuous(
# limits = c(0, 30),
# breaks = c(0, 10, 20, 30),
# name = expression('Mother CEWL (g '*m^-2*' '*h^-1*')')
# ) +
scale_y_continuous(
#limits = c(0, 21),
breaks = c(0, 10, 20),
name = expression('Neonate CEWL (g '*m^-2*' '*h^-1*')')
) -> neo_VPD_CEWL_partreg
neo_VPD_CEWL_partreg
DAB osml clutch ~ mother
this is what was done originally, but we now use all neonate data and do not avg by clutch
DAB_osml_LM_1 <- lm(data = dab_clutch_mom,
Plasma_Osmol_Rep_mean_mean ~
Treatment *
(Mother_osml + Mother_Days_in_Treatment) +
Mass_g_mean)
car::vif(DAB_osml_LM_1, type = "predictor")## GVIF Df GVIF^(1/(2*Df))
## Treatment 1.316106 5 1.027848
## Mother_osml 9.171779 3 1.446801
## Mother_Days_in_Treatment 1004.349474 3 3.164566
## Mass_g_mean 1.316106 1 1.147217
## Interacts With
## Treatment Mother_osml, Mother_Days_in_Treatment
## Mother_osml Treatment
## Mother_Days_in_Treatment Treatment
## Mass_g_mean --
## Other Predictors
## Treatment Mass_g_mean
## Mother_osml Mother_Days_in_Treatment, Mass_g_mean
## Mother_Days_in_Treatment Mother_osml, Mass_g_mean
## Mass_g_mean Treatment, Mother_osml, Mother_Days_in_Treatment## Single term deletions
##
## Model:
## Plasma_Osmol_Rep_mean_mean ~ Treatment * (Mother_osml + Mother_Days_in_Treatment) +
## Mass_g_mean
## Df Sum of Sq RSS AIC
## <none> 1168.5 89.116
## Mass_g_mean 1 1.917 1170.4 87.145
## Treatment:Mother_osml 1 6.144 1174.6 87.210
## Treatment:Mother_Days_in_Treatment 1 154.172 1322.7 89.347## Analysis of Variance Table
##
## Response: Plasma_Osmol_Rep_mean_mean
## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 1 811.93 811.93 7.6434 0.01840 *
## Mother_osml 1 392.60 392.60 3.6959 0.08081 .
## Mother_Days_in_Treatment 1 133.48 133.48 1.2566 0.28617
## Mass_g_mean 1 1.02 1.02 0.0096 0.92361
## Treatment:Mother_osml 1 4.04 4.04 0.0380 0.84896
## Treatment:Mother_Days_in_Treatment 1 154.17 154.17 1.4514 0.25359
## Residuals 11 1168.49 106.23
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1DAB_osml_LM_2a <- lm(data = dab_clutch_mom,
Plasma_Osmol_Rep_mean_mean ~
Treatment * Mother_Days_in_Treatment +
Mother_osml +
Mass_g_mean)
drop1(DAB_osml_LM_2a)## Single term deletions
##
## Model:
## Plasma_Osmol_Rep_mean_mean ~ Treatment * Mother_Days_in_Treatment +
## Mother_osml + Mass_g_mean
## Df Sum of Sq RSS AIC
## <none> 1174.6 87.210
## Mother_osml 1 43.603 1218.2 85.866
## Mass_g_mean 1 5.030 1179.7 85.287
## Treatment:Mother_Days_in_Treatment 1 152.067 1326.7 87.401## Anova Table (Type II tests)
##
## Response: Plasma_Osmol_Rep_mean_mean
## Sum Sq Df F value Pr(>F)
## Treatment 455.34 1 4.6517 0.05201 .
## Mother_Days_in_Treatment 124.13 1 1.2681 0.28215
## Mother_osml 43.60 1 0.4454 0.51714
## Mass_g_mean 5.03 1 0.0514 0.82449
## Treatment:Mother_Days_in_Treatment 152.07 1 1.5535 0.23640
## Residuals 1174.63 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1DAB_osml_LM_2b <- lm(data = dab_clutch_mom,
Plasma_Osmol_Rep_mean_mean ~
Treatment *
(Mother_osml + Mother_Days_in_Treatment))
drop1(DAB_osml_LM_2b)## Single term deletions
##
## Model:
## Plasma_Osmol_Rep_mean_mean ~ Treatment * (Mother_osml + Mother_Days_in_Treatment)
## Df Sum of Sq RSS AIC
## <none> 1170.4 87.145
## Treatment:Mother_osml 1 9.256 1179.7 85.287
## Treatment:Mother_Days_in_Treatment 1 152.289 1322.7 87.347## Anova Table (Type II tests)
##
## Response: Plasma_Osmol_Rep_mean_mean
## Sum Sq Df F value Pr(>F)
## Treatment 507.00 1 5.1982 0.04168 *
## Mother_osml 58.46 1 0.5994 0.45381
## Mother_Days_in_Treatment 118.37 1 1.2136 0.29222
## Treatment:Mother_osml 9.26 1 0.0949 0.76332
## Treatment:Mother_Days_in_Treatment 152.29 1 1.5614 0.23528
## Residuals 1170.41 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1DAB_osml_LM_3 <- lm(data = dab_clutch_mom,
Plasma_Osmol_Rep_mean_mean ~
Treatment * Mother_Days_in_Treatment +
Mother_osml)
drop1(DAB_osml_LM_3)## Single term deletions
##
## Model:
## Plasma_Osmol_Rep_mean_mean ~ Treatment * Mother_Days_in_Treatment +
## Mother_osml
## Df Sum of Sq RSS AIC
## <none> 1179.7 85.287
## Mother_osml 1 58.457 1238.1 84.158
## Treatment:Mother_Days_in_Treatment 1 148.060 1327.7 85.415## Anova Table (Type II tests)
##
## Response: Plasma_Osmol_Rep_mean_mean
## Sum Sq Df F value Pr(>F)
## Treatment 507.00 1 5.5872 0.03434 *
## Mother_Days_in_Treatment 133.48 1 1.4710 0.24677
## Mother_osml 58.46 1 0.6442 0.43662
## Treatment:Mother_Days_in_Treatment 148.06 1 1.6316 0.22382
## Residuals 1179.66 13
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1DAB_osml_LM_4 <- lm(data = dab_clutch_mom,
Plasma_Osmol_Rep_mean_mean ~
Treatment * Mother_Days_in_Treatment)
drop1(DAB_osml_LM_4)## Single term deletions
##
## Model:
## Plasma_Osmol_Rep_mean_mean ~ Treatment * Mother_Days_in_Treatment
## Df Sum of Sq RSS AIC
## <none> 1238.1 84.158
## Treatment:Mother_Days_in_Treatment 1 225.53 1463.7 85.170## Anova Table (Type II tests)
##
## Response: Plasma_Osmol_Rep_mean_mean
## Sum Sq Df F value Pr(>F)
## Treatment 828.57 1 9.3690 0.008465 **
## Mother_Days_in_Treatment 390.16 1 4.4117 0.054293 .
## Treatment:Mother_Days_in_Treatment 225.53 1 2.5502 0.132602
## Residuals 1238.12 14
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1DAB_osml_LM_5 <- lm(data = dab_clutch_mom,
Plasma_Osmol_Rep_mean_mean ~
Treatment + Mother_Days_in_Treatment)
drop1(DAB_osml_LM_5)## Single term deletions
##
## Model:
## Plasma_Osmol_Rep_mean_mean ~ Treatment + Mother_Days_in_Treatment
## Df Sum of Sq RSS AIC
## <none> 1463.7 85.170
## Treatment 1 828.57 2292.2 91.244
## Mother_Days_in_Treatment 1 390.16 1853.8 87.423## Anova Table (Type II tests)
##
## Response: Plasma_Osmol_Rep_mean_mean
## Sum Sq Df F value Pr(>F)
## Treatment 828.57 1 8.4914 0.01069 *
## Mother_Days_in_Treatment 390.16 1 3.9985 0.06399 .
## Residuals 1463.65 15
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1DAB_osml_LM_6 <- lm(data = dab_clutch_mom,
Plasma_Osmol_Rep_mean_mean ~
Treatment)
car::Anova(DAB_osml_LM_6, type = "2")## Anova Table (Type II tests)
##
## Response: Plasma_Osmol_Rep_mean_mean
## Sum Sq Df F value Pr(>F)
## Treatment 811.93 1 7.0077 0.01757 *
## Residuals 1853.80 16
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Model Selection
clutch_osmol_models_all <- list(DAB_osml_LM_1, DAB_osml_LM_2a, DAB_osml_LM_3, DAB_osml_LM_4, DAB_osml_LM_5, DAB_osml_LM_6, DAB_osml_LM_null)
#specify model names
clutch_osmol_mod_names_all <- c('model1',
'model2',
'model3',
'model4',
'model5',
'model6',
'modelnull')
#calculate AIC of each model
clutch_osmol_AICc_all <- data.frame(aictab(cand.set = clutch_osmol_models_all,
modnames = clutch_osmol_mod_names_all))
clutch_osmol_AICc_all## Modnames K AICc Delta_AICc ModelLik AICcWt LL Cum.Wt
## 5 model5 4 141.3284 0.0000000 1.000000000 4.028689e-01 -65.12573 0.4028689
## 6 model6 3 142.2193 0.8908989 0.640536320 2.580522e-01 -67.25250 0.6609211
## 4 model4 5 142.2394 0.9109883 0.634134522 2.554731e-01 -63.61969 0.9163942
## 7 modelnull 2 145.8433 4.5149020 0.104616812 4.214686e-02 -70.52165 0.9585410
## 3 model3 6 146.0052 4.6767763 0.096483027 3.887001e-02 -63.18440 0.9974111
## 2 model2 7 151.4919 10.1635002 0.006209033 2.501427e-03 -63.14595 0.9999125
## 1 model1 8 158.1975 16.8691074 0.000217230 8.751523e-05 -63.09875 1.0000000The best top/equivalent models are 5 (tmt + mother days in tmt), 6 (tmt only), and 4 (tmt * mother days in tmt).
Final Model
DAB_osml_LM_BEST <- lm(data = dab_clutch_mom,
Plasma_Osmol_Rep_mean_mean ~
Treatment)
car::Anova(DAB_osml_LM_BEST, type = "2")## Anova Table (Type II tests)
##
## Response: Plasma_Osmol_Rep_mean_mean
## Sum Sq Df F value Pr(>F)
## Treatment 811.93 1 7.0077 0.01757 *
## Residuals 1853.80 16
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Call:
## lm(formula = Plasma_Osmol_Rep_mean_mean ~ Treatment, data = dab_clutch_mom)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.3615 -8.7975 0.3219 7.8067 20.5719
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 304.095 3.806 79.906 <2e-16 ***
## TreatmentW -13.516 5.106 -2.647 0.0176 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.76 on 16 degrees of freedom
## Multiple R-squared: 0.3046, Adjusted R-squared: 0.2611
## F-statistic: 7.008 on 1 and 16 DF, p-value: 0.01757DAB_osml_LM_BEST_add <- lm(data = dab_clutch_mom,
Plasma_Osmol_Rep_mean_mean ~
Treatment + Mother_Days_in_Treatment)
car::Anova(DAB_osml_LM_BEST_add, type = "2")## Anova Table (Type II tests)
##
## Response: Plasma_Osmol_Rep_mean_mean
## Sum Sq Df F value Pr(>F)
## Treatment 828.57 1 8.4914 0.01069 *
## Mother_Days_in_Treatment 390.16 1 3.9985 0.06399 .
## Residuals 1463.65 15
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Call:
## lm(formula = Plasma_Osmol_Rep_mean_mean ~ Treatment + Mother_Days_in_Treatment,
## data = dab_clutch_mom)
##
## Residuals:
## Min 1Q Median 3Q Max
## -15.0217 -7.4365 0.6639 5.2247 15.3004
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 295.9690 5.3582 55.236 <2e-16 ***
## TreatmentW -13.6553 4.6861 -2.914 0.0107 *
## Mother_Days_in_Treatment 0.9287 0.4644 2.000 0.0640 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.878 on 15 degrees of freedom
## Multiple R-squared: 0.4509, Adjusted R-squared: 0.3777
## F-statistic: 6.16 on 2 and 15 DF, p-value: 0.01115DAB_osml_LM_BEST_int <- lm(data = dab_clutch_mom,
Plasma_Osmol_Rep_mean_mean ~
Treatment * Mother_Days_in_Treatment)
car::Anova(DAB_osml_LM_BEST_int, type = "2")## Anova Table (Type II tests)
##
## Response: Plasma_Osmol_Rep_mean_mean
## Sum Sq Df F value Pr(>F)
## Treatment 828.57 1 9.3690 0.008465 **
## Mother_Days_in_Treatment 390.16 1 4.4117 0.054293 .
## Treatment:Mother_Days_in_Treatment 225.53 1 2.5502 0.132602
## Residuals 1238.12 14
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Call:
## lm(formula = Plasma_Osmol_Rep_mean_mean ~ Treatment * Mother_Days_in_Treatment,
## data = dab_clutch_mom)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.708 -8.931 -0.686 7.514 11.664
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 290.9261 5.9995 48.492 <2e-16 ***
## TreatmentW -0.9141 9.1412 -0.100 0.9218
## Mother_Days_in_Treatment 1.5050 0.5707 2.637 0.0195 *
## TreatmentW:Mother_Days_in_Treatment -1.4413 0.9026 -1.597 0.1326
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.404 on 14 degrees of freedom
## Multiple R-squared: 0.5355, Adjusted R-squared: 0.436
## F-statistic: 5.381 on 3 and 14 DF, p-value: 0.01128
DAB OSML DIFFS
first, check whether tmt and mother osml are collinear
##
## Call:
## lm(formula = Mother_osml ~ Treatment, data = dab_all)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.093 -6.093 0.366 11.240 39.490
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 317.760 2.632 120.719 <2e-16 ***
## TreatmentW -8.875 3.493 -2.541 0.013 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.57 on 79 degrees of freedom
## Multiple R-squared: 0.07555, Adjusted R-squared: 0.06385
## F-statistic: 6.457 on 1 and 79 DF, p-value: 0.01301Yep, as expected, so I think I need to run one model selection with tmt and one with mother osml??
Tmt + Mom Osml
DAB_OSMOL_LMM_1 <- lme4::lmer(data = dab_all,
Plasma_Osmol_Rep_mean ~
Treatment *
(Mother_osml+Mother_Days_in_Treatment+live_neonates+total_embryos) +
Mass_g +
(1|Mother_ID))
car::vif(DAB_OSMOL_LMM_1) ## Treatment Mother_osml
## 933.860788 4.451064
## Mother_Days_in_Treatment live_neonates
## 4.156945 21.171490
## total_embryos Mass_g
## 15.974730 1.506775
## Treatment:Mother_osml Treatment:Mother_Days_in_Treatment
## 984.238714 17.840094
## Treatment:live_neonates Treatment:total_embryos
## 383.663677 290.979234## Single term deletions
##
## Model:
## Plasma_Osmol_Rep_mean ~ Treatment * (Mother_osml + Mother_Days_in_Treatment +
## live_neonates + total_embryos) + Mass_g + (1 | Mother_ID)
## npar AIC
## <none> 653.29
## Mass_g 1 651.64
## Treatment:Mother_osml 1 652.27
## Treatment:Mother_Days_in_Treatment 1 652.13
## Treatment:live_neonates 1 654.19
## Treatment:total_embryos 1 653.14DAB_OSMOL_LMM_1a <- lme4::lmer(data = dab_all,
Plasma_Osmol_Rep_mean ~
Treatment *
(live_neonates + total_embryos) +
Mother_osml + Mother_Days_in_Treatment +
Mass_g +
(1|Mother_ID))
car::vif(DAB_OSMOL_LMM_1a) ## Treatment live_neonates total_embryos
## 29.200490 19.241148 15.155303
## Mother_osml Mother_Days_in_Treatment Mass_g
## 2.698235 2.927528 1.480098
## Treatment:live_neonates Treatment:total_embryos
## 216.170484 156.894196## Single term deletions
##
## Model:
## Plasma_Osmol_Rep_mean ~ Treatment * (live_neonates + total_embryos) +
## Mother_osml + Mother_Days_in_Treatment + Mass_g + (1 | Mother_ID)
## npar AIC
## <none> 650.44
## Mother_osml 1 648.47
## Mother_Days_in_Treatment 1 657.15
## Mass_g 1 648.84
## Treatment:live_neonates 1 651.09
## Treatment:total_embryos 1 649.84DAB_OSMOL_LMM_1b <- lme4::lmer(data = dab_all,
Plasma_Osmol_Rep_mean ~
Treatment +
Mother_osml + Mother_Days_in_Treatment +
live_neonates + total_embryos +
Mass_g +
(1|Mother_ID))
car::vif(DAB_OSMOL_LMM_1b) ## Treatment Mother_osml Mother_Days_in_Treatment
## 1.254321 1.571342 2.059891
## live_neonates total_embryos Mass_g
## 11.244267 12.120990 1.313391## Single term deletions
##
## Model:
## Plasma_Osmol_Rep_mean ~ Treatment + Mother_osml + Mother_Days_in_Treatment +
## live_neonates + total_embryos + Mass_g + (1 | Mother_ID)
## npar AIC
## <none> 649.78
## Treatment 1 652.99
## Mother_osml 1 649.47
## Mother_Days_in_Treatment 1 653.29
## live_neonates 1 653.84
## total_embryos 1 652.06
## Mass_g 1 648.20## Analysis of Variance Table
## npar Sum Sq Mean Sq F value
## Treatment 1 1156.42 1156.42 9.1689
## Mother_osml 1 588.65 588.65 4.6672
## Mother_Days_in_Treatment 1 197.60 197.60 1.5667
## live_neonates 1 174.73 174.73 1.3854
## total_embryos 1 511.74 511.74 4.0574
## Mass_g 1 105.88 105.88 0.8395the two different clutch size assessments are too collinear, I need to choose one…
test_live <- lme4::lmer(data = dab_all,
Plasma_Osmol_Rep_mean ~
Treatment +
Mother_osml +
live_neonates +
(1|Mother_ID))
summary(test_live)## Linear mixed model fit by REML ['lmerMod']
## Formula: Plasma_Osmol_Rep_mean ~ Treatment + Mother_osml + live_neonates +
## (1 | Mother_ID)
## Data: dab_all
##
## REML criterion at convergence: 632.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7921 -0.5028 -0.0780 0.5456 3.2112
##
## Random effects:
## Groups Name Variance Std.Dev.
## Mother_ID (Intercept) 68.0 8.246
## Residual 128.3 11.327
## Number of obs: 81, groups: Mother_ID, 18
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 217.1458 48.3527 4.491
## TreatmentW -10.4905 4.9302 -2.128
## Mother_osml 0.2992 0.1491 2.007
## live_neonates -1.0530 1.1398 -0.924
##
## Correlation of Fixed Effects:
## (Intr) TrtmnW Mthr_s
## TreatmentW -0.366
## Mother_osml -0.977 0.300
## live_neonts -0.171 0.095 -0.031## Analysis of Variance Table
## npar Sum Sq Mean Sq F value
## Treatment 1 995.25 995.25 7.7569
## Mother_osml 1 502.92 502.92 3.9197
## live_neonates 1 109.49 109.49 0.8534test_total <- lme4::lmer(data = dab_all,
Plasma_Osmol_Rep_mean ~
Treatment +
Mother_osml +
total_embryos +
(1|Mother_ID))
summary(test_total)## Linear mixed model fit by REML ['lmerMod']
## Formula: Plasma_Osmol_Rep_mean ~ Treatment + Mother_osml + total_embryos +
## (1 | Mother_ID)
## Data: dab_all
##
## REML criterion at convergence: 633.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8000 -0.4869 -0.0634 0.5624 3.2034
##
## Random effects:
## Groups Name Variance Std.Dev.
## Mother_ID (Intercept) 71.81 8.474
## Residual 128.30 11.327
## Number of obs: 81, groups: Mother_ID, 18
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 215.693 49.879 4.324
## TreatmentW -10.629 5.109 -2.081
## Mother_osml 0.292 0.152 1.921
## total_embryos -0.545 0.998 -0.546
##
## Correlation of Fixed Effects:
## (Intr) TrtmnW Mthr_s
## TreatmentW -0.386
## Mother_osml -0.979 0.306
## total_mbrys -0.228 0.201 0.038## Analysis of Variance Table
## npar Sum Sq Mean Sq F value
## Treatment 1 960.17 960.17 7.4839
## Mother_osml 1 484.42 484.42 3.7758
## total_embryos 1 38.25 38.25 0.2982I think it makes sense to use the live neonates since mothers might have stopped devoting resources to slugs.
start good models
(good = no collinearity)
DAB_OSMOL_LMM_1c <- lme4::lmer(data = dab_all,
Plasma_Osmol_Rep_mean ~
Treatment +
Mother_osml + Mother_Days_in_Treatment +
live_neonates +
Mass_g +
(1|Mother_ID))
car::vif(DAB_OSMOL_LMM_1c) ## Treatment Mother_osml Mother_Days_in_Treatment
## 1.217221 1.508605 1.361143
## live_neonates Mass_g
## 1.138326 1.182775## Single term deletions
##
## Model:
## Plasma_Osmol_Rep_mean ~ Treatment + Mother_osml + Mother_Days_in_Treatment +
## live_neonates + Mass_g + (1 | Mother_ID)
## npar AIC
## <none> 652.06
## Treatment 1 655.10
## Mother_osml 1 652.50
## Mother_Days_in_Treatment 1 651.77
## live_neonates 1 652.82
## Mass_g 1 652.22## Analysis of Variance Table
## npar Sum Sq Mean Sq F value
## Treatment 1 954.34 954.34 7.6772
## Mother_osml 1 481.36 481.36 3.8723
## Mother_Days_in_Treatment 1 161.88 161.88 1.3022
## live_neonates 1 145.43 145.43 1.1699
## Mass_g 1 274.90 274.90 2.2114DAB_OSMOL_LMM_2 <- lme4::lmer(data = dab_all,
Plasma_Osmol_Rep_mean ~
Treatment +
Mother_osml +
live_neonates +
Mass_g +
(1|Mother_ID))
drop1(DAB_OSMOL_LMM_2)## Single term deletions
##
## Model:
## Plasma_Osmol_Rep_mean ~ Treatment + Mother_osml + live_neonates +
## Mass_g + (1 | Mother_ID)
## npar AIC
## <none> 651.77
## Treatment 1 653.56
## Mother_osml 1 655.31
## live_neonates 1 652.04
## Mass_g 1 652.42## Analysis of Variance Table
## npar Sum Sq Mean Sq F value
## Treatment 1 933.85 933.85 7.5226
## Mother_osml 1 470.58 470.58 3.7908
## live_neonates 1 102.96 102.96 0.8294
## Mass_g 1 326.01 326.01 2.6262DAB_OSMOL_LMM_3 <- lme4::lmer(data = dab_all,
Plasma_Osmol_Rep_mean ~
Treatment +
Mother_osml +
Mass_g +
(1|Mother_ID))
DAB_OSMOL_LMM_3a <- lme4::lmer(data = dab_all,
Plasma_Osmol_Rep_mean ~
Treatment +
Mother_osml +
live_neonates +
(1|Mother_ID))
summary(DAB_OSMOL_LMM_3)## Linear mixed model fit by REML ['lmerMod']
## Formula: Plasma_Osmol_Rep_mean ~ Treatment + Mother_osml + Mass_g + (1 |
## Mother_ID)
## Data: dab_all
##
## REML criterion at convergence: 632.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.83638 -0.51605 -0.05075 0.60473 3.11669
##
## Random effects:
## Groups Name Variance Std.Dev.
## Mother_ID (Intercept) 75.01 8.661
## Residual 125.14 11.186
## Number of obs: 81, groups: Mother_ID, 18
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 217.2706 49.5555 4.384
## TreatmentW -8.7918 5.1715 -1.700
## Mother_osml 0.3209 0.1551 2.069
## Mass_g -1.0816 0.8693 -1.244
##
## Correlation of Fixed Effects:
## (Intr) TrtmnW Mthr_s
## TreatmentW -0.322
## Mother_osml -0.964 0.324
## Mass_g -0.127 -0.200 -0.133## Single term deletions
##
## Model:
## Plasma_Osmol_Rep_mean ~ Treatment + Mother_osml + Mass_g + (1 |
## Mother_ID)
## npar AIC
## <none> 652.04
## Treatment 1 653.38
## Mother_osml 1 654.78
## Mass_g 1 651.46## Analysis of Variance Table
## npar Sum Sq Mean Sq F value
## Treatment 1 916.99 916.99 7.3279
## Mother_osml 1 461.74 461.74 3.6898
## Mass_g 1 193.73 193.73 1.5481## Analysis of Variance Table
## npar Sum Sq Mean Sq F value
## Treatment 1 995.25 995.25 7.7569
## Mother_osml 1 502.92 502.92 3.9197
## live_neonates 1 109.49 109.49 0.8534DAB_OSMOL_LMM_4 <- lme4::lmer(data = dab_all,
Plasma_Osmol_Rep_mean ~
Treatment +
Mother_osml +
(1|Mother_ID))
drop1(DAB_OSMOL_LMM_4)## Single term deletions
##
## Model:
## Plasma_Osmol_Rep_mean ~ Treatment + Mother_osml + (1 | Mother_ID)
## npar AIC
## <none> 651.46
## Treatment 1 653.97
## Mother_osml 1 653.70## Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)
##
## Response: Plasma_Osmol_Rep_mean
## F Df Df.res Pr(>F)
## Treatment 4.2611 1 14.764 0.05703 .
## Mother_osml 3.9788 1 15.258 0.06426 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Model Selection
osml_models_all <- list(DAB_OSMOL_LMM_1c, DAB_OSMOL_LMM_2,
DAB_OSMOL_LMM_3, DAB_OSMOL_LMM_3a,
DAB_OSMOL_LMM_4, DAB_OSMOL_LMM_5,
DAB_OSMOL_LMM_null)
#specify model names
osml_mod_names_all <- c('model1', 'model2',
'model3', 'model3a', 'model4', 'model5',
'modelnull')
#calculate AIC of each model
osml_AICc_all <- data.frame(aictab(cand.set = osml_models_all,
modnames = osml_mod_names_all))
osml_AICc_all## Modnames K AICc Delta_AICc ModelLik AICcWt Res.LL Cum.Wt
## 2 model2 7 643.6723 0.0000000 1.000000000 0.309640917 -314.0690 0.3096409
## 1 model1 8 644.3037 0.6313507 0.729296184 0.225819939 -313.1518 0.5354609
## 3 model3 6 645.4118 1.7395217 0.419051753 0.129755569 -316.1384 0.6652164
## 4 model3a 6 645.5000 1.8276648 0.400984542 0.124161221 -316.1824 0.7893776
## 6 model5 4 645.6023 1.9299350 0.380995582 0.117971821 -318.5380 0.9073495
## 5 model4 5 646.1160 2.4436508 0.294691750 0.091248624 -317.6580 0.9985981
## 7 modelnull 3 654.4675 10.7951590 0.004527526 0.001401907 -324.0779 1.0000000They are literally all equivalent… lol
Tmt only
DAB_OSMOL_tmt_LMM_1 <- lme4::lmer(data = dab_all,
Plasma_Osmol_Rep_mean ~
Treatment *
(Mother_Days_in_Treatment + live_neonates) +
Mass_g +
(1|Mother_ID))
car::vif(DAB_OSMOL_tmt_LMM_1) ## Treatment Mother_Days_in_Treatment
## 20.882880 1.748671
## live_neonates Mass_g
## 2.240056 1.394589
## Treatment:Mother_Days_in_Treatment Treatment:live_neonates
## 6.147350 22.128174## Single term deletions
##
## Model:
## Plasma_Osmol_Rep_mean ~ Treatment * (Mother_Days_in_Treatment +
## live_neonates) + Mass_g + (1 | Mother_ID)
## npar AIC
## <none> 651.14
## Mass_g 1 651.31
## Treatment:Mother_Days_in_Treatment 1 649.49
## Treatment:live_neonates 1 653.30DAB_OSMOL_tmt_LMM_1a <- lme4::lmer(data = dab_all,
Plasma_Osmol_Rep_mean ~
Treatment *
Mother_Days_in_Treatment +
live_neonates +
Mass_g +
(1|Mother_ID))
car::vif(DAB_OSMOL_tmt_LMM_1a) ## Treatment Mother_Days_in_Treatment
## 4.638489 1.744984
## live_neonates Mass_g
## 1.293482 1.220707
## Treatment:Mother_Days_in_Treatment
## 5.676306## Single term deletions
##
## Model:
## Plasma_Osmol_Rep_mean ~ Treatment * Mother_Days_in_Treatment +
## live_neonates + Mass_g + (1 | Mother_ID)
## npar AIC
## <none> 653.30
## live_neonates 1 652.55
## Mass_g 1 651.96
## Treatment:Mother_Days_in_Treatment 1 652.50DAB_OSMOL_tmt_LMM_2a <- lme4::lmer(data = dab_all,
Plasma_Osmol_Rep_mean ~
Treatment *
Mother_Days_in_Treatment +
live_neonates +
(1|Mother_ID))
car::vif(DAB_OSMOL_tmt_LMM_2a) ## Treatment Mother_Days_in_Treatment
## 4.502250 1.688108
## live_neonates Treatment:Mother_Days_in_Treatment
## 1.118255 5.291212## Single term deletions
##
## Model:
## Plasma_Osmol_Rep_mean ~ Treatment * Mother_Days_in_Treatment +
## live_neonates + (1 | Mother_ID)
## npar AIC
## <none> 651.96
## live_neonates 1 650.72
## Treatment:Mother_Days_in_Treatment 1 651.93DAB_OSMOL_tmt_LMM_2b <- lme4::lmer(data = dab_all,
Plasma_Osmol_Rep_mean ~
Treatment +
Mother_Days_in_Treatment +
live_neonates +
Mass_g +
(1|Mother_ID))
car::vif(DAB_OSMOL_tmt_LMM_2b) ## Treatment Mother_Days_in_Treatment live_neonates
## 1.035046 1.019044 1.135896
## Mass_g
## 1.139650## Single term deletions
##
## Model:
## Plasma_Osmol_Rep_mean ~ Treatment + Mother_Days_in_Treatment +
## live_neonates + Mass_g + (1 | Mother_ID)
## npar AIC
## <none> 652.50
## Treatment 1 658.21
## Mother_Days_in_Treatment 1 655.31
## live_neonates 1 652.93
## Mass_g 1 651.93DAB_OSMOL_tmt_LMM_3a <- lme4::lmer(data = dab_all,
Plasma_Osmol_Rep_mean ~
Treatment *
Mother_Days_in_Treatment +
(1|Mother_ID))
drop1(DAB_OSMOL_tmt_LMM_3a)## Single term deletions
##
## Model:
## Plasma_Osmol_Rep_mean ~ Treatment * Mother_Days_in_Treatment +
## (1 | Mother_ID)
## npar AIC
## <none> 650.72
## Treatment:Mother_Days_in_Treatment 1 651.49DAB_OSMOL_tmt_LMM_3b <- lme4::lmer(data = dab_all,
Plasma_Osmol_Rep_mean ~
Treatment +
Mother_Days_in_Treatment +
live_neonates +
(1|Mother_ID))
drop1(DAB_OSMOL_tmt_LMM_3b)## Single term deletions
##
## Model:
## Plasma_Osmol_Rep_mean ~ Treatment + Mother_Days_in_Treatment +
## live_neonates + (1 | Mother_ID)
## npar AIC
## <none> 651.93
## Treatment 1 658.90
## Mother_Days_in_Treatment 1 654.97
## live_neonates 1 651.49DAB_OSMOL_tmt_LMM_4 <- lme4::lmer(data = dab_all,
Plasma_Osmol_Rep_mean ~
Treatment +
Mother_Days_in_Treatment +
(1|Mother_ID))
drop1(DAB_OSMOL_tmt_LMM_4)## Single term deletions
##
## Model:
## Plasma_Osmol_Rep_mean ~ Treatment + Mother_Days_in_Treatment +
## (1 | Mother_ID)
## npar AIC
## <none> 651.49
## Treatment 1 657.43
## Mother_Days_in_Treatment 1 653.70## Analysis of Variance Table
## npar Sum Sq Mean Sq F value
## Treatment 1 1010.05 1010.05 7.8605
## Mother_Days_in_Treatment 1 507.32 507.32 3.9481Model Selection
osml_tmt_models_all <- list(DAB_OSMOL_tmt_LMM_1a,
DAB_OSMOL_tmt_LMM_2a, DAB_OSMOL_tmt_LMM_2b,
DAB_OSMOL_tmt_LMM_3a, DAB_OSMOL_tmt_LMM_3b,
DAB_OSMOL_tmt_LMM_4, DAB_OSMOL_tmt_LMM_5,
DAB_OSMOL_tmt_LMM_null)
#specify model names
osml_tmt_mod_names_all <- c('model1', 'model2a', 'model2b',
'model3a', 'model3b', 'model4', 'model5',
'modelnull')
#calculate AIC of each model
data.frame(aictab(cand.set = osml_tmt_models_all,
modnames = osml_tmt_mod_names_all))## Modnames K AICc Delta_AICc ModelLik AICcWt Res.LL Cum.Wt
## 1 model1 8 641.8093 0.0000000 1.000000000 0.2108152701 -311.9047 0.2108153
## 2 model2a 7 641.9359 0.1266004 0.938661650 0.1978842093 -313.2009 0.4086995
## 3 model2b 7 642.0460 0.2366023 0.888428466 0.1872942871 -313.2559 0.5959938
## 4 model3a 6 642.2566 0.4472416 0.799618302 0.1685717483 -314.5607 0.7645655
## 5 model3b 6 642.8059 0.9965279 0.607584552 0.1280881015 -314.8354 0.8926536
## 6 model4 5 643.8676 2.0582860 0.357313048 0.0753270467 -316.5338 0.9679807
## 7 model5 4 645.6023 3.7929112 0.150099690 0.0316433066 -318.5380 0.9996240
## 8 modelnull 3 654.4675 12.6581353 0.001783696 0.0003760304 -324.0779 1.0000000Mom Osml only
DAB_OSMOL_mom_LMM_1 <- lme4::lmer(data = dab_all,
Plasma_Osmol_Rep_mean ~
Mother_osml +
Mother_Days_in_Treatment + live_neonates +
Mass_g +
(1|Mother_ID))
car::vif(DAB_OSMOL_mom_LMM_1) ## Mother_osml Mother_Days_in_Treatment live_neonates
## 1.281577 1.286981 1.124035
## Mass_g
## 1.122335## Single term deletions
##
## Model:
## Plasma_Osmol_Rep_mean ~ Mother_osml + Mother_Days_in_Treatment +
## live_neonates + Mass_g + (1 | Mother_ID)
## npar AIC
## <none> 655.10
## Mother_osml 1 658.21
## Mother_Days_in_Treatment 1 653.56
## live_neonates 1 655.09
## Mass_g 1 656.84DAB_OSMOL_mom_LMM_2 <- lme4::lmer(data = dab_all,
Plasma_Osmol_Rep_mean ~
Mother_osml +
live_neonates +
Mass_g +
(1|Mother_ID))
drop1(DAB_OSMOL_mom_LMM_2)## Single term deletions
##
## Model:
## Plasma_Osmol_Rep_mean ~ Mother_osml + live_neonates + Mass_g +
## (1 | Mother_ID)
## npar AIC
## <none> 653.56
## Mother_osml 1 659.09
## live_neonates 1 653.38
## Mass_g 1 655.46## Analysis of Variance Table
## npar Sum Sq Mean Sq F value
## Mother_osml 1 782.58 782.58 6.3179
## live_neonates 1 56.14 56.14 0.4533
## Mass_g 1 464.00 464.00 3.7460DAB_OSMOL_mom_LMM_3 <- lme4::lmer(data = dab_all,
Plasma_Osmol_Rep_mean ~
Mother_osml +
Mass_g +
(1|Mother_ID))
drop1(DAB_OSMOL_mom_LMM_3)## Single term deletions
##
## Model:
## Plasma_Osmol_Rep_mean ~ Mother_osml + Mass_g + (1 | Mother_ID)
## npar AIC
## <none> 653.38
## Mother_osml 1 658.03
## Mass_g 1 653.97DAB_OSMOL_mom_LMM_4 <- lme4::lmer(data = dab_all,
Plasma_Osmol_Rep_mean ~
Mother_osml +
(1|Mother_ID))
anova(DAB_OSMOL_mom_LMM_4, test = "F", type = "2")## Analysis of Variance Table
## npar Sum Sq Mean Sq F value
## Mother_osml 1 824.95 824.95 6.4084Model Selection
osml_mom_models_all <- list(DAB_OSMOL_mom_LMM_1,
DAB_OSMOL_mom_LMM_2,
DAB_OSMOL_mom_LMM_3,
DAB_OSMOL_mom_LMM_4,
DAB_OSMOL_mom_LMM_null)
#specify model names
osml_mom_mod_names_all <- c('model1', 'model2',
'model3', 'model4',
'modelnull')
#calculate AIC of each model
data.frame(aictab(cand.set = osml_mom_models_all,
modnames = osml_mom_mod_names_all))## Modnames K AICc Delta_AICc ModelLik AICcWt Res.LL Cum.Wt
## 2 model2 6 649.3303 0.000000 1.00000000 0.45058843 -318.0976 0.4505884
## 1 model1 7 650.6159 1.285680 0.52579701 0.23691805 -317.5408 0.6875065
## 3 model3 5 650.9535 1.623237 0.44413854 0.20012369 -320.0767 0.8876302
## 4 model4 4 652.8422 3.511903 0.17274283 0.07783592 -322.1579 0.9654661
## 5 modelnull 3 654.4675 5.137226 0.07664179 0.03453390 -324.0779 1.0000000Final Modelsss…
Okay, so if we use either tmt or mother osml, then the top models are one with Treatment * Mother_Days_in_Treatment and one with only Mother_osml!
DAB_OSMOL_LMM_BEST_tmt <- lmerTest::lmer(data = dab_all,
Plasma_Osmol_Rep_mean ~
Treatment * Mother_Days_in_Treatment +
(1|Mother_ID))
DAB_OSMOL_LMM_BEST_mom <- lmerTest::lmer(data = dab_all,
Plasma_Osmol_Rep_mean ~
Mother_osml +
(1|Mother_ID))
anova(DAB_OSMOL_LMM_BEST_tmt, test = "F", type = "2")## Type II Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Treatment 2.49 2.49 1 13.444 0.0194 0.89119
## Mother_Days_in_Treatment 534.04 534.04 1 14.319 4.1664 0.06011
## Treatment:Mother_Days_in_Treatment 301.39 301.39 1 14.248 2.3514 0.14707
##
## Treatment
## Mother_Days_in_Treatment .
## Treatment:Mother_Days_in_Treatment
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Type II Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Mother_osml 824.95 824.95 1 16.028 6.4084 0.0222 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Plasma_Osmol_Rep_mean ~ Treatment * Mother_Days_in_Treatment +
## (1 | Mother_ID)
## Data: dab_all
##
## REML criterion at convergence: 629.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9034 -0.5272 -0.1034 0.6063 3.1029
##
## Random effects:
## Groups Name Variance Std.Dev.
## Mother_ID (Intercept) 59.6 7.72
## Residual 128.2 11.32
## Number of obs: 81, groups: Mother_ID, 18
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 291.0394 5.9030 13.2003 49.303
## TreatmentW -1.2598 9.0369 13.4438 -0.139
## Mother_Days_in_Treatment 1.4761 0.5751 14.4829 2.567
## TreatmentW:Mother_Days_in_Treatment -1.3886 0.9056 14.2483 -1.533
## Pr(>|t|)
## (Intercept) 2.34e-16 ***
## TreatmentW 0.8912
## Mother_Days_in_Treatment 0.0219 *
## TreatmentW:Mother_Days_in_Treatment 0.1471
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) TrtmnW M_D__T
## TreatmentW -0.653
## Mthr_Dys__T -0.824 0.538
## TrtW:M_D__T 0.523 -0.869 -0.635## $emmeans
## Mother_Days_in_Treatment = 8.33:
## Treatment emmean SE df lower.CL upper.CL
## C 303 3.35 14.2 296 311
## W 291 2.98 13.7 284 297
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## Mother_Days_in_Treatment = 8.33:
## contrast estimate SE df t.ratio p.value
## C - W 12.8 4.48 14 2.864 0.0125
##
## Degrees-of-freedom method: kenward-rogerosml_tmt_trends <- emtrends(DAB_OSMOL_LMM_BEST_tmt,
var = "Mother_Days_in_Treatment", "Treatment")
tidy(osml_tmt_trends)## # A tibble: 2 × 6
## Treatment Mother_Days_in_Treatment.trend std.error df statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 C 1.48 0.575 14.4 2.57 0.0220
## 2 W 0.0875 0.700 14.0 0.125 0.902## contrast estimate SE df t.ratio p.value
## C - W 1.39 0.906 14.2 1.533 0.1472
##
## Degrees-of-freedom method: kenward-rogerDAB_OSMOL_emmeans_tmt <- emmeans(DAB_OSMOL_LMM_BEST_tmt,
"Treatment", by = "Mother_Days_in_Treatment")
DAB_OSMOL_emmeans_CI <- tidy(confint(DAB_OSMOL_emmeans_tmt))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Plasma_Osmol_Rep_mean ~ Mother_osml + (1 | Mother_ID)
## Data: dab_all
##
## REML criterion at convergence: 644.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6944 -0.4909 -0.1177 0.5547 3.2989
##
## Random effects:
## Groups Name Variance Std.Dev.
## Mother_ID (Intercept) 85.15 9.228
## Residual 128.73 11.346
## Number of obs: 81, groups: Mother_ID, 18
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 174.3195 48.2604 15.9829 3.612 0.00234 **
## Mother_osml 0.3895 0.1538 16.0276 2.531 0.02220 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Mother_osml -0.999## R2m R2c
## [1,] 0.155193 0.4915269## R2m R2c
## [1,] 0.2675243 0.5000087Model Assumptions
##
## Shapiro-Wilk normality test
##
## data: residuals(DAB_OSMOL_LMM_BEST_tmt)
## W = 0.97279, p-value = 0.08199
##
## Shapiro-Wilk normality test
##
## data: residuals(DAB_OSMOL_LMM_BEST_mom)
## W = 0.97018, p-value = 0.05555looks great
PUB FIG
osml ~ mom osml
ggplot(
dab_clutch_mom,
aes(
x = Mother_osml,
y = Plasma_Osmol_Rep_mean_mean
)
) +
geom_abline(
slope = 1, intercept = 0,
linetype = "dashed", color = "darkgrey"
) +
geom_pointrange(
aes(ymin = Plasma_Osmol_Rep_mean_mean-Plasma_Osmol_Rep_mean_sd,
ymax = Plasma_Osmol_Rep_mean_mean+Plasma_Osmol_Rep_mean_sd,
color = Treatment, shape = Treatment),
alpha = 0.6, size = 0.2, linewidth = 0.3, show.legend = FALSE
) +
geom_point(aes(color = Treatment, shape = Treatment)) +
geom_smooth(
se = T,
formula = 'y~x',
method = "lm",
linewidth = 1,
color = "black",
show.legend = FALSE
) +
# prettys
scale_color_manual(
values = c("darkgoldenrod2", "royalblue4"),
labels = c("C" = "Control", "W" = "Hydration"),
name = NULL
) +
scale_shape_manual(
values = c(15, 17),
labels = c("C" = "Control", "W" = "Hydration"),
name = NULL
) +
theme(
legend.position = "inside",
legend.position.inside = c(0.75, 0.15),
legend.background = element_blank()
) +
scale_x_continuous(
limits = c(260, 360),
breaks = c(260, 280, 300, 320, 340, 360),
name = expression('Mother Osmolality (mmol '*kg^-1*')'),
) +
scale_y_continuous(
limits = c(260, 360),
breaks = c(260, 280, 300, 320, 340, 360),
name = expression('Neonate Osmolality (mmol '*kg^-1*')'),
) -> neo_mom_osml
neo_mom_osml
export it:
ggsave(
filename = "Fig5_neonate_mother_osml.pdf",
plot = neo_mom_osml,
path = "./results_figures",
device = "pdf",
dpi = 600,
units = "mm",
width = 150,
height = 100
)
ggsave(
filename = "Fig5_neonate_mother_osml.tiff",
plot = neo_mom_osml,
path = "./results_figures",
device = "tiff",
dpi = 600,
units = "mm",
width = 150,
height = 100
)osml ~ tmt * days
ggplot(
dab_clutch_mom,
aes(
x = Mother_Days_in_Treatment,
y = Plasma_Osmol_Rep_mean_mean,
color = Treatment,
shape = Treatment
)
) +
# geom_abline(
# slope = 1, intercept = 0,
# linetype = "dashed", color = "darkgrey"
# ) +
geom_pointrange(
aes(ymin = Plasma_Osmol_Rep_mean_mean-Plasma_Osmol_Rep_mean_sd,
ymax = Plasma_Osmol_Rep_mean_mean+Plasma_Osmol_Rep_mean_sd),
alpha = 0.6, size = 0.2, linewidth = 0.3, show.legend = FALSE
) +
geom_point() +
geom_smooth(
se = F,
formula = 'y~x',
method = "lm",
linewidth = 1,
show.legend = FALSE
) +
# prettys
scale_color_manual(
values = c("darkgoldenrod2", "royalblue4"),
labels = c("C" = "Control", "W" = "Hydration"),
name = NULL
) +
scale_shape_manual(
values = c(15, 17),
labels = c("C" = "Control", "W" = "Hydration"),
name = NULL
) +
theme(
legend.position = "inside",
legend.position.inside = c(0.15, 0.85),
legend.background = element_blank()
) +
scale_x_continuous(
limits = c(2, 15),
breaks = c(2, 6, 10, 14),
name = "Mother Days in Treatment Before Birthing"
) +
scale_y_continuous(
limits = c(269, 360),
breaks = c(280, 300, 320, 340, 360),
name = expression('Neonate Osmolality (mmol '*kg^-1*')'),
) -> neo_tmt_osml
neo_tmt_osml
PUB FIG 3 DAB
Osmolality
ggplot() +
geom_pointrange(
data = dab_all_clutch_avg,
aes(
x = Treatment,
y = Plasma_Osmol_Rep_mean_mean,
ymin = Plasma_Osmol_Rep_mean_min,
ymax = Plasma_Osmol_Rep_mean_max,
group = Mother_ID,
color = Treatment,
shape = Treatment
),
position = position_jitter(width = 0.3),
size = 0.2, linewidth = 0.3,
alpha = 0.8,
show.legend = FALSE
) +
geom_errorbar(
data = DAB_OSMOL_emmeans_CI,
aes(x = Treatment, ymin = conf.low, ymax = conf.high,
color = Treatment),
width = 0.3
) +
geom_point(
data = DAB_OSMOL_emmeans_CI,
aes(x = Treatment, y = estimate,
color = Treatment, shape = Treatment),
size = 2.5
) +
# prettys
scale_color_manual(
values = c("darkgoldenrod2", "royalblue4"),
labels = c("C" = "Control", "W" = "Hydration"),
name = NULL
) +
scale_shape_manual(
values = c(15, 17),
labels = c("C" = "Control", "W" = "Hydration"),
name = NULL
) +
labs(
x = NULL,
y = expression('Osmolality (mmol '*kg^-1*')'),
color = "Treatment"
) +
scale_y_continuous(
limits = c(260, 360),
breaks = seq(260, 360, by = 20)
) +
scale_x_discrete(labels = c("C" = "Control", "W" = "Hydration")) +
theme(legend.position = "none") +
# statistical labels
annotate(geom = "text", x = 1.5, y = 340, label = "*", size = 6) -> dab_osml
dab_osml
CEWL
ggplot() +
geom_pointrange(
data = dab_all_clutch_avg,
aes(
x = Treatment,
y = CEWL_g_m2h_mean,
ymin = CEWL_g_m2h_min,
ymax = CEWL_g_m2h_max,
group = Mother_ID,
color = Treatment,
shape = Treatment
),
position = position_jitter(width = 0.3),
size = 0.2, linewidth = 0.3,
alpha = 0.8,
show.legend = FALSE
) +
geom_errorbar(
data = DAB_CEWL_emmeans_CI,
aes(x = Treatment, ymin = conf.low, ymax = conf.high,
color = Treatment),
width = 0.3
) +
geom_point(
data = DAB_CEWL_emmeans_CI,
aes(x = Treatment, y = estimate,
color = Treatment, shape = Treatment),
size = 2.5
) +
# prettys
scale_color_manual(
values = c("darkgoldenrod2", "royalblue4"),
labels = c("C" = "Control", "W" = "Hydration"),
name = NULL
) +
scale_shape_manual(
values = c(15, 17),
labels = c("C" = "Control", "W" = "Hydration"),
name = NULL
) +
labs(
x = NULL,
y = expression('CEWL (g '*m^-2*' '*h^-1*')'),
color = "Treatment"
) +
scale_y_continuous(
limits = c(4, 25),
breaks = seq(0, 30, by = 5)
) +
scale_x_discrete(labels = c("C" = "Control", "W" = "Hydration")) +
theme(legend.position = "none") +
# statistical labels
annotate(geom = "text", x = 1.5, y = 22, label = "NS", size = 2) -> dab_cewl
dab_cewl
Arrange
set.seed(135) # to get a nice jitter: 10, 35, 44, 359, 135
ggarrange(
dab_osml, dab_cewl,
nrow = 2,
ncol = 1,
labels = c("A", "B"),
align = "hv"
) -> dab_diffs
ggsave(
filename = "Fig3_neonate_dab_diffs.pdf",
plot = dab_diffs,
path = "./results_figures",
device = "pdf",
dpi = 600,
units = "mm",
width = 80,
height = 120
)
ggsave(
filename = "Fig3_neonate_dab_diffs.tiff",
plot = dab_diffs,
path = "./results_figures",
device = "tiff",
dpi = 600,
units = "mm",
width = 80,
height = 120
)TMT EFFECTS
Calculate Deltas
Only 3 individuals were for sure measured at the before and after time points, so the change from before to after cannot be calculated for individual neonates.
deltas_means <- dab_ps_compare_means %>%
arrange(Mother_ID, timept) %>%
group_by(Mother_ID) %>%
mutate(
delta_mass = Mass_g_mean - lag(Mass_g_mean),
delta_osml = Plasma_Osmol_Rep_mean_mean - lag(Plasma_Osmol_Rep_mean_mean),
delta_CEWL = CEWL_g_m2h_mean - lag(CEWL_g_m2h_mean)
)
deltas_befaft <- deltas_means %>%
ungroup() %>%
mutate(
delta_mass = case_when(is.na(delta_mass) ~ 0, TRUE ~ delta_mass),
delta_osml = case_when(is.na(delta_osml) ~ 0, TRUE ~ delta_osml),
delta_CEWL = case_when(is.na(delta_CEWL) ~ 0, TRUE ~ delta_CEWL)
)
deltas <- deltas_means %>%
filter(complete.cases(delta_mass))
summary(deltas_befaft)## Mother_ID Treatment timept n Tail_Length_cm_mean
## 124 :2 C:8 DAB:8 Min. :3.000 Min. :1.560
## 125 :2 W:8 PS :8 1st Qu.:4.750 1st Qu.:1.775
## 128 :2 Median :5.000 Median :1.880
## 129 :2 Mean :4.625 Mean :1.865
## 131 :2 3rd Qu.:5.000 3rd Qu.:1.962
## 133 :2 Max. :5.000 Max. :2.160
## (Other):4
## SVL_cm_mean Mass_g_mean Tb_CEWL_c_mean Plasma_Osmol_Rep_mean_mean
## Min. :21.80 Min. :11.66 Min. :27.42 Min. :280.5
## 1st Qu.:22.31 1st Qu.:13.07 1st Qu.:27.96 1st Qu.:286.2
## Median :23.35 Median :14.14 Median :28.60 Median :296.6
## Mean :23.17 Mean :14.40 Mean :28.58 Mean :304.2
## 3rd Qu.:23.72 3rd Qu.:15.07 3rd Qu.:29.23 3rd Qu.:312.5
## Max. :25.87 Max. :18.46 Max. :29.92 Max. :363.4
##
## CEWL_g_m2h_mean msmt_temp_C_mean msmt_RH_percent_mean VPD_kPa_mean
## Min. : 7.296 Min. :23.71 Min. :36.80 Min. :1.595
## 1st Qu.:10.795 1st Qu.:25.40 1st Qu.:39.02 1st Qu.:1.923
## Median :15.046 Median :25.75 Median :40.10 Median :1.962
## Mean :13.959 Mean :25.65 Mean :40.39 Mean :1.965
## 3rd Qu.:16.193 3rd Qu.:26.01 3rd Qu.:41.24 3rd Qu.:2.060
## Max. :17.244 Max. :26.88 Max. :45.57 Max. :2.160
##
## Tail_Length_cm_sd SVL_cm_sd Mass_g_sd Tb_CEWL_c_sd
## Min. :0.1517 Min. :0.4435 Min. :0.2380 Min. :0.3000
## 1st Qu.:0.1930 1st Qu.:0.6132 1st Qu.:0.4584 1st Qu.:0.4959
## Median :0.2334 Median :0.7606 Median :0.6737 Median :0.9314
## Mean :0.2407 Mean :0.7866 Mean :0.8755 Mean :0.9654
## 3rd Qu.:0.2842 3rd Qu.:0.9680 3rd Qu.:1.2115 3rd Qu.:1.3562
## Max. :0.3808 Max. :1.0970 Max. :2.0768 Max. :1.8050
##
## Plasma_Osmol_Rep_mean_sd CEWL_g_m2h_sd msmt_temp_C_sd msmt_RH_percent_sd
## Min. : 2.739 Min. :1.128 Min. :0.1311 Min. :0.1248
## 1st Qu.: 6.355 1st Qu.:1.765 1st Qu.:0.1642 1st Qu.:0.3391
## Median : 8.009 Median :2.213 Median :0.1990 Median :0.6041
## Mean :12.556 Mean :2.577 Mean :0.2269 Mean :0.8738
## 3rd Qu.:14.390 3rd Qu.:3.172 3rd Qu.:0.2876 3rd Qu.:1.2300
## Max. :43.634 Max. :5.040 Max. :0.3929 Max. :2.6074
##
## VPD_kPa_sd Tail_Length_cm_min SVL_cm_min Mass_g_min
## Min. :0.009671 Min. :1.200 Min. :20.50 Min. :11.10
## 1st Qu.:0.028434 1st Qu.:1.500 1st Qu.:21.38 1st Qu.:11.90
## Median :0.037982 Median :1.500 Median :22.45 Median :13.05
## Mean :0.044378 Mean :1.562 Mean :22.18 Mean :13.23
## 3rd Qu.:0.062645 3rd Qu.:1.700 3rd Qu.:23.00 3rd Qu.:14.28
## Max. :0.110722 Max. :1.900 Max. :24.60 Max. :16.60
##
## Tb_CEWL_c_min Plasma_Osmol_Rep_mean_min CEWL_g_m2h_min msmt_temp_C_min
## Min. :25.80 Min. :242.7 Min. : 4.745 Min. :23.56
## 1st Qu.:26.98 1st Qu.:279.5 1st Qu.: 9.571 1st Qu.:25.06
## Median :27.40 Median :286.2 Median :11.494 Median :25.52
## Mean :27.50 Mean :290.2 Mean :10.959 Mean :25.40
## 3rd Qu.:27.93 3rd Qu.:296.2 3rd Qu.:12.982 3rd Qu.:25.76
## Max. :29.50 Max. :332.5 Max. :14.812 Max. :26.70
##
## msmt_RH_percent_min VPD_kPa_min Tail_Length_cm_max SVL_cm_max
## Min. :35.15 Min. :1.566 Min. :2.000 Min. :22.40
## 1st Qu.:37.66 1st Qu.:1.858 1st Qu.:2.000 1st Qu.:23.45
## Median :39.23 Median :1.917 Median :2.050 Median :24.00
## Mean :39.44 Mean :1.911 Mean :2.119 Mean :24.11
## 3rd Qu.:40.96 3rd Qu.:1.972 3rd Qu.:2.200 3rd Qu.:24.62
## Max. :44.34 Max. :2.144 Max. :2.500 Max. :26.50
##
## Mass_g_max Tb_CEWL_c_max Plasma_Osmol_Rep_mean_max CEWL_g_m2h_max
## Min. :12.10 Min. :28.10 Min. :283.5 Min. : 9.705
## 1st Qu.:13.70 1st Qu.:29.35 1st Qu.:297.5 1st Qu.:13.229
## Median :14.70 Median :30.20 Median :306.2 Median :18.466
## Mean :15.25 Mean :29.83 Mean :318.5 Mean :17.158
## 3rd Qu.:16.38 3rd Qu.:30.43 3rd Qu.:327.8 3rd Qu.:19.771
## Max. :20.00 Max. :31.30 Max. :428.0 Max. :23.808
##
## msmt_temp_C_max msmt_RH_percent_max VPD_kPa_max delta_mass
## Min. :24.02 Min. :37.77 Min. :1.641 Min. :-2.4800
## 1st Qu.:25.68 1st Qu.:40.20 1st Qu.:1.963 1st Qu.:-1.4400
## Median :25.99 Median :41.31 Median :2.001 Median :-0.4100
## Mean :25.93 Mean :41.46 Mean :2.014 Mean :-0.8094
## 3rd Qu.:26.29 3rd Qu.:42.48 3rd Qu.:2.138 3rd Qu.: 0.0000
## Max. :27.20 Max. :46.22 Max. :2.179 Max. : 0.0000
##
## delta_osml delta_CEWL
## Min. :-18.17 Min. :-1.215
## 1st Qu.: 0.00 1st Qu.: 0.000
## Median : 0.00 Median : 0.000
## Mean : 10.75 Mean : 2.193
## 3rd Qu.: 14.84 3rd Qu.: 5.339
## Max. : 72.97 Max. : 9.816
## ## Mother_ID Treatment timept n Tail_Length_cm_mean
## 124 :1 C:4 DAB:0 Min. :3.000 Min. :1.680
## 125 :1 W:4 PS :8 1st Qu.:3.750 1st Qu.:1.775
## 128 :1 Median :5.000 Median :1.890
## 129 :1 Mean :4.375 Mean :1.890
## 131 :1 3rd Qu.:5.000 3rd Qu.:1.983
## 133 :1 Max. :5.000 Max. :2.160
## (Other):2
## SVL_cm_mean Mass_g_mean Tb_CEWL_c_mean Plasma_Osmol_Rep_mean_mean
## Min. :23.33 Min. :11.66 Min. :27.47 Min. :282.2
## 1st Qu.:23.48 1st Qu.:12.52 1st Qu.:27.75 1st Qu.:289.3
## Median :23.64 Median :13.26 Median :28.67 Median :313.0
## Mean :23.94 Mean :13.59 Mean :28.54 Mean :314.9
## 3rd Qu.:24.07 3rd Qu.:13.80 3rd Qu.:29.28 3rd Qu.:335.1
## Max. :25.87 Max. :17.53 Max. :29.54 Max. :363.4
##
## CEWL_g_m2h_mean msmt_temp_C_mean msmt_RH_percent_mean VPD_kPa_mean
## Min. :14.57 Min. :25.07 Min. :36.80 Min. :1.924
## 1st Qu.:15.57 1st Qu.:25.49 1st Qu.:37.87 1st Qu.:1.953
## Median :16.34 Median :25.62 Median :39.35 Median :1.967
## Mean :16.15 Mean :25.67 Mean :39.21 Mean :2.003
## 3rd Qu.:17.07 3rd Qu.:26.00 3rd Qu.:40.12 3rd Qu.:2.060
## Max. :17.24 Max. :26.02 Max. :41.79 Max. :2.124
##
## Tail_Length_cm_sd SVL_cm_sd Mass_g_sd Tb_CEWL_c_sd
## Min. :0.1517 Min. :0.4435 Min. :0.2775 Min. :0.3000
## 1st Qu.:0.1930 1st Qu.:0.5511 1st Qu.:0.4584 1st Qu.:0.4632
## Median :0.2121 Median :0.6728 Median :0.6055 Median :0.7047
## Mean :0.2143 Mean :0.7485 Mean :0.8381 Mean :0.7398
## 3rd Qu.:0.2255 3rd Qu.:0.9966 3rd Qu.:0.9867 3rd Qu.:0.9812
## Max. :0.2881 Max. :1.0970 Max. :1.9655 Max. :1.2973
##
## Plasma_Osmol_Rep_mean_sd CEWL_g_m2h_sd msmt_temp_C_sd msmt_RH_percent_sd
## Min. : 4.174 Min. :1.595 Min. :0.1311 Min. :0.1248
## 1st Qu.: 7.866 1st Qu.:2.560 1st Qu.:0.1925 1st Qu.:0.3391
## Median :11.468 Median :2.967 Median :0.2689 Median :0.5981
## Mean :17.693 Mean :2.983 Mean :0.2629 Mean :0.8022
## 3rd Qu.:24.097 3rd Qu.:3.565 3rd Qu.:0.3181 3rd Qu.:1.2300
## Max. :43.634 Max. :4.161 Max. :0.3929 Max. :1.7985
##
## VPD_kPa_sd Tail_Length_cm_min SVL_cm_min Mass_g_min
## Min. :0.02112 Min. :1.500 Min. :22.40 Min. :11.10
## 1st Qu.:0.03340 1st Qu.:1.500 1st Qu.:22.75 1st Qu.:11.47
## Median :0.04012 Median :1.550 Median :23.00 Median :12.15
## Mean :0.04599 Mean :1.625 Mean :23.07 Mean :12.54
## 3rd Qu.:0.06512 3rd Qu.:1.725 3rd Qu.:23.05 3rd Qu.:13.07
## Max. :0.06985 Max. :1.900 Max. :24.60 Max. :15.30
##
## Tb_CEWL_c_min Plasma_Osmol_Rep_mean_min CEWL_g_m2h_min msmt_temp_C_min
## Min. :26.50 Min. :242.7 Min. :10.86 Min. :24.65
## 1st Qu.:27.23 1st Qu.:283.4 1st Qu.:11.53 1st Qu.:25.27
## Median :27.60 Median :296.2 Median :12.28 Median :25.40
## Mean :27.70 Mean :296.3 Mean :12.63 Mean :25.38
## 3rd Qu.:28.02 3rd Qu.:317.9 3rd Qu.:13.87 3rd Qu.:25.64
## Max. :29.10 Max. :332.5 Max. :14.81 Max. :25.77
##
## msmt_RH_percent_min VPD_kPa_min Tail_Length_cm_max SVL_cm_max
## Min. :36.20 Min. :1.859 Min. :2.000 Min. :24.00
## 1st Qu.:37.01 1st Qu.:1.908 1st Qu.:2.000 1st Qu.:24.00
## Median :38.16 Median :1.925 Median :2.150 Median :24.25
## Mean :38.48 Mean :1.945 Mean :2.125 Mean :24.77
## 3rd Qu.:39.74 3rd Qu.:1.969 3rd Qu.:2.200 3rd Qu.:25.55
## Max. :41.52 Max. :2.063 Max. :2.300 Max. :26.50
##
## Mass_g_max Tb_CEWL_c_max Plasma_Osmol_Rep_mean_max CEWL_g_m2h_max
## Min. :12.10 Min. :28.10 Min. :296.0 Min. :18.27
## 1st Qu.:12.95 1st Qu.:28.27 1st Qu.:300.2 1st Qu.:18.69
## Median :14.00 Median :29.85 Median :325.5 Median :19.23
## Mean :14.35 Mean :29.44 Mean :334.9 Mean :19.73
## 3rd Qu.:14.97 3rd Qu.:30.30 3rd Qu.:352.2 3rd Qu.:20.86
## Max. :19.00 Max. :30.60 Max. :428.0 Max. :21.93
##
## msmt_temp_C_max msmt_RH_percent_max VPD_kPa_max delta_mass
## Min. :25.43 Min. :37.77 Min. :1.951 Min. :-2.480
## 1st Qu.:25.82 1st Qu.:39.60 1st Qu.:1.996 1st Qu.:-2.235
## Median :25.99 Median :40.64 Median :2.009 Median :-1.520
## Mean :25.96 Mean :40.29 Mean :2.048 Mean :-1.619
## 3rd Qu.:26.14 3rd Qu.:41.18 3rd Qu.:2.138 3rd Qu.:-1.119
## Max. :26.35 Max. :42.35 Max. :2.149 Max. :-0.820
##
## delta_osml delta_CEWL
## Min. :-18.17 Min. :-1.215
## 1st Qu.: 2.30 1st Qu.: 1.772
## Median : 19.85 Median : 5.459
## Mean : 21.50 Mean : 4.387
## 3rd Qu.: 35.42 3rd Qu.: 5.942
## Max. : 72.97 Max. : 9.816
## MASS
mass_lm_time_tmt <- lmerTest::lmer(data = dab_ps_compare,
Mass_g ~ timept * Treatment + (1|Mother_ID))
r.squaredGLMM(mass_lm_time_tmt)## R2m R2c
## [1,] 0.1457843 0.817281mass_means_time <- emmeans(mass_lm_time_tmt,
pairwise ~ timept | Treatment)
mass_means_time$contrasts## Treatment = C:
## contrast estimate SE df t.ratio p.value
## DAB - PS 2.16 0.319 64 6.760 <.0001
##
## Treatment = W:
## contrast estimate SE df t.ratio p.value
## DAB - PS 1.08 0.339 64 3.197 0.0022
##
## Degrees-of-freedom method: kenward-roger## timept = DAB:
## contrast estimate SE df t.ratio p.value
## C - W 0.218 1.38 6.31 0.158 0.8798
##
## timept = PS:
## contrast estimate SE df t.ratio p.value
## C - W -0.853 1.39 6.40 -0.614 0.5606
##
## Degrees-of-freedom method: kenward-rogermass_means <- emmeans(mass_lm_time_tmt, "Treatment", by = "timept")
mass_lm_time_tmt_CI <- tidy(confint(mass_means))
#mass_lm_time_tmt_CI <- tidy(mass_means_time$emmeans) # either would work
anova(mass_lm_time_tmt, test = "F", type = "2")## Type II Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## timept 49.904 49.904 1 64.010 50.4859 1.214e-09 ***
## Treatment 0.043 0.043 1 5.984 0.0440 0.84090
## timept:Treatment 5.234 5.234 1 64.012 5.2954 0.02465 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1MASS CHANGE
##
## Call:
## lm(formula = delta_mass ~ Treatment, data = deltas)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.31500 -0.15812 -0.08292 0.17250 0.48500
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.1650 0.1487 -14.560 6.58e-06 ***
## TreatmentW 1.0925 0.2103 5.195 0.00202 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2974 on 6 degrees of freedom
## Multiple R-squared: 0.8181, Adjusted R-squared: 0.7878
## F-statistic: 26.99 on 1 and 6 DF, p-value: 0.002024## Anova Table (Type II tests)
##
## Response: delta_mass
## Sum Sq Df F value Pr(>F)
## Treatment 2.38711 1 26.99 0.002024 **
## Residuals 0.53066 6
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## $emmeans
## Treatment emmean SE df lower.CL upper.CL
## C -2.17 0.149 6 -2.53 -1.801
## W -1.07 0.149 6 -1.44 -0.709
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## C - W -1.09 0.21 6 -5.195 0.0020OSML
osml_lm_time_tmt <- lmerTest::lmer(data = dab_ps_compare,
Plasma_Osmol_Rep_mean ~ timept * Treatment + (1|Mother_ID))
r.squaredGLMM(osml_lm_time_tmt)## R2m R2c
## [1,] 0.6248089 0.6374795osml_means_time <- emmeans(osml_lm_time_tmt,
pairwise ~ timept | Treatment)
osml_means_time$contrasts## Treatment = C:
## contrast estimate SE df t.ratio p.value
## DAB - PS -44.98 5.30 64.2 -8.484 <.0001
##
## Treatment = W:
## contrast estimate SE df t.ratio p.value
## DAB - PS 2.46 5.63 65.0 0.436 0.6644
##
## Degrees-of-freedom method: kenward-roger## timept = DAB:
## contrast estimate SE df t.ratio p.value
## C - W 5.13 5.73 16.1 0.894 0.3845
##
## timept = PS:
## contrast estimate SE df t.ratio p.value
## C - W 52.56 6.06 18.6 8.680 <.0001
##
## Degrees-of-freedom method: kenward-rogerosml_means <- emmeans(osml_lm_time_tmt, "Treatment", by = "timept")
osml_lm_time_tmt_CI <- tidy(confint(osml_means))
anova(osml_lm_time_tmt, test = "F", type = "2")## Type II Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## timept 9418.2 9418.2 1 64.945 34.452 1.625e-07 ***
## Treatment 10504.4 10504.4 1 6.297 38.425 0.000675 ***
## timept:Treatment 10315.9 10315.9 1 64.989 37.735 5.492e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1OSML CHANGE
##
## Call:
## lm(formula = delta_osml ~ Treatment, data = deltas)
##
## Residuals:
## Min 1Q Median 3Q Max
## -16.160 -15.311 -1.113 10.368 26.948
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 46.027 8.664 5.312 0.00181 **
## TreatmentW -49.062 12.253 -4.004 0.00709 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.33 on 6 degrees of freedom
## Multiple R-squared: 0.7277, Adjusted R-squared: 0.6823
## F-statistic: 16.03 on 1 and 6 DF, p-value: 0.007087## Anova Table (Type II tests)
##
## Response: delta_osml
## Sum Sq Df F value Pr(>F)
## Treatment 4814.2 1 16.032 0.007087 **
## Residuals 1801.7 6
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## $emmeans
## Treatment emmean SE df lower.CL upper.CL
## C 46.03 8.66 6 24.8 67.2
## W -3.04 8.66 6 -24.2 18.2
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## C - W 49.1 12.3 6 4.004 0.0071CEWL
NOTE I did test the inclusion of Tb, msmt temp, and msmt VPD in this model specifically, and none had an influence on CEWL compared to the time difference.
cewl_lm_time_tmt <- lmerTest::lmer(data = dab_ps_compare,
CEWL_g_m2h ~ timept * Treatment + (1|Mother_ID))
r.squaredGLMM(cewl_lm_time_tmt)## R2m R2c
## [1,] 0.3063975 0.3942069cewl_means_time <- emmeans(cewl_lm_time_tmt,
pairwise ~ timept | Treatment)
cewl_means_time$contrasts## Treatment = C:
## contrast estimate SE df t.ratio p.value
## DAB - PS -3.67 1.03 63.3 -3.563 0.0007
##
## Treatment = W:
## contrast estimate SE df t.ratio p.value
## DAB - PS -4.64 1.08 63.6 -4.295 0.0001
##
## Degrees-of-freedom method: kenward-roger## timept = DAB:
## contrast estimate SE df t.ratio p.value
## C - W 1.950 1.34 11.4 1.458 0.1718
##
## timept = PS:
## contrast estimate SE df t.ratio p.value
## C - W 0.981 1.38 12.6 0.712 0.4895
##
## Degrees-of-freedom method: kenward-rogercewl_means <- emmeans(cewl_lm_time_tmt, "Treatment", by = "timept")
cewl_lm_time_tmt_CI <- tidy(confint(cewl_means))
anova(cewl_lm_time_tmt, test = "F", type = "2")## Type II Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## timept 309.391 309.391 1 63.603 30.8195 5.913e-07 ***
## Treatment 17.295 17.295 1 6.122 1.7228 0.2364
## timept:Treatment 4.233 4.233 1 63.615 0.4217 0.5185
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1CEWL CHANGE
##
## Call:
## lm(formula = delta_CEWL ~ Treatment, data = deltas)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.2523 -1.9639 0.7369 1.9903 4.7785
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.736 1.825 2.047 0.0866 .
## TreatmentW 1.301 2.581 0.504 0.6322
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.651 on 6 degrees of freedom
## Multiple R-squared: 0.04062, Adjusted R-squared: -0.1193
## F-statistic: 0.254 on 1 and 6 DF, p-value: 0.6322## Anova Table (Type II tests)
##
## Response: delta_CEWL
## Sum Sq Df F value Pr(>F)
## Treatment 3.386 1 0.254 0.6322
## Residuals 79.967 6## $emmeans
## Treatment emmean SE df lower.CL upper.CL
## C 3.74 1.83 6 -0.730 8.2
## W 5.04 1.83 6 0.571 9.5
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## C - W -1.3 2.58 6 -0.504 0.6322Tb
tb_lm_time_tmt <- lmerTest::lmer(data = dab_ps_compare,
Tb_CEWL_c ~ timept * Treatment + (1|Mother_ID))
r.squaredGLMM(tb_lm_time_tmt)## R2m R2c
## [1,] 0.05646733 0.05646733## Treatment = C:
## contrast estimate SE df t.ratio p.value
## DAB - PS -0.471 0.394 64.3 -1.196 0.2362
##
## Treatment = W:
## contrast estimate SE df t.ratio p.value
## DAB - PS 0.486 0.418 65.3 1.161 0.2498
##
## Degrees-of-freedom method: kenward-roger## timept = DAB:
## contrast estimate SE df t.ratio p.value
## C - W -0.0921 0.394 19.9 -0.234 0.8175
##
## timept = PS:
## contrast estimate SE df t.ratio p.value
## C - W 0.8648 0.419 22.9 2.063 0.0506
##
## Degrees-of-freedom method: kenward-rogertb_means <- emmeans(tb_lm_time_tmt, "Treatment", by = "timept")
tb_lm_time_tmt_CI <- tidy(confint(tb_means))
anova(tb_lm_time_tmt, test = "F", type = "2")## Type II Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## timept 0.0074 0.0074 1 70 0.0049 0.94432
## Treatment 2.3737 2.3737 1 70 1.5721 0.21407
## timept:Treatment 4.2049 4.2049 1 70 2.7850 0.09962 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Shedding time
Is there a difference in time between birth and shedding based on tmt group?
date_tmt <- dab_ps_compare %>%
group_by(Mother_ID, Treatment) %>%
summarise(
min_date = min(Date_Born_Shed),
max_date = max(Date_Born_Shed)
) %>%
mutate(range = as.numeric(max_date - min_date)) %>%
arrange(range)
shedtimelm <- lm(data = date_tmt, range ~ Treatment)
summary(shedtimelm)##
## Call:
## lm(formula = range ~ Treatment, data = date_tmt)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.0000 -0.4375 -0.2500 0.1875 2.0000
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.0000 0.5303 11.31 2.85e-05 ***
## TreatmentW -0.7500 0.7500 -1.00 0.356
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.061 on 6 degrees of freedom
## Multiple R-squared: 0.1429, Adjusted R-squared: -2.22e-16
## F-statistic: 1 on 1 and 6 DF, p-value: 0.3559## Analysis of Variance Table
##
## Response: range
## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 1 1.125 1.125 1 0.3559
## Residuals 6 6.750 1.125PUB FIG 6
mass raw
ggplot() +
geom_pointrange(
data = dab_ps_compare_means,
aes(
x = timept,
y = Mass_g_mean,
ymin = Mass_g_mean-Mass_g_sd,
ymax = Mass_g_mean+Mass_g_sd,
group = Treatment,
color = Treatment,
shape = Treatment
),
position = position_jitterdodge(jitter.width = 0.6, dodge.width = 0.6),
size = 0.2, linewidth = 0.3,
alpha = 0.6,
show.legend = FALSE
) +
geom_line(
data = mass_lm_time_tmt_CI,
aes(
x = timept, y = estimate,
group = Treatment,
color = Treatment
),
position = position_dodge(width = 0.6)
) +
geom_errorbar(
data = mass_lm_time_tmt_CI,
aes(
x = timept, y = estimate,
ymin = conf.low, ymax = conf.high,
group = Treatment,
color = Treatment
),
width = 0.4,
position = position_dodge(width = 0.6)
) +
geom_point(
data = mass_lm_time_tmt_CI,
aes(
x = timept, y = estimate,
group = Treatment,
color = Treatment,
shape = Treatment
),
size = 2.5,
position = position_dodge(width = 0.6)
) +
# prettys
scale_color_manual(
values = c("darkgoldenrod2", "royalblue4"),
labels = c("C" = "Control", "W" = "Hydration"),
name = "Treatment"
) +
scale_shape_manual(
values = c(15, 17),
labels = c("C" = "Control", "W" = "Hydration"),
name = "Treatment"
) +
scale_y_continuous(
limits = c(10, 21),
breaks = c(10, 15, 20),
name = "Mass (g)"
) +
scale_x_discrete(
labels = c("DAB" = "At Birth", "PS" = "Post-Shed"),
name = NULL
) +
theme(legend.position = "none") +
# statistical labels
annotate(geom = "text", x = 1, y = 21, label = "NS", size = 2) +
annotate(geom = "text", x = 2, y = 21, label = "NS", size = 2) +
annotate(geom = "text", x = 2.5, y = 12.8, label = "*", size = 6,
color = "darkgoldenrod2") +
annotate(geom = "text", x = 2.5, y = 13.7, label = "*", size = 6,
color = "royalblue4") -> neo_mass # save
neo_mass
osmol raw
ggplot() +
geom_pointrange(
data = dab_ps_compare_means,
aes(
x = timept,
y = Plasma_Osmol_Rep_mean_mean,
ymin = Plasma_Osmol_Rep_mean_mean-Plasma_Osmol_Rep_mean_sd,
ymax = Plasma_Osmol_Rep_mean_mean+Plasma_Osmol_Rep_mean_sd,
group = Treatment,
color = Treatment,
shape = Treatment
),
position = position_jitterdodge(jitter.width = 0.6, dodge.width = 0.6),
size = 0.2, linewidth = 0.3,
alpha = 0.6,
show.legend = FALSE
) +
geom_line(
data = osml_lm_time_tmt_CI,
aes(
x = timept, y = estimate,
group = Treatment,
color = Treatment
),
position = position_dodge(width = 0.6)
) +
geom_errorbar(
data = osml_lm_time_tmt_CI,
aes(
x = timept, y = estimate,
ymin = conf.low, ymax = conf.high,
group = Treatment,
color = Treatment
),
width = 0.4,
position = position_dodge(width = 0.6)
) +
geom_point(
data = osml_lm_time_tmt_CI,
aes(
x = timept, y = estimate,
group = Treatment,
color = Treatment,
shape = Treatment
),
size = 2.5,
position = position_dodge(width = 0.6)
) +
# prettys
scale_color_manual(
values = c("darkgoldenrod2", "royalblue4"),
labels = c("C" = "Control", "W" = "Hydration"),
name = "Treatment"
) +
scale_shape_manual(
values = c(15, 17),
labels = c("C" = "Control", "W" = "Hydration"),
name = "Treatment"
) +
scale_y_continuous(
limits = c(240, 410),
breaks = seq(200, 400, by = 50),
name = expression('Osmolality (mmol '*kg^-1*')'),
) +
scale_x_discrete(
labels = c("DAB" = "At Birth", "PS" = "Post-Shed"),
name = NULL
) +
theme(legend.position = "none") +
# statistical labels
annotate(geom = "text", x = 1, y = 405, label = "NS", size = 2) +
annotate(geom = "text", x = 2, y = 400, label = "*", size = 6) +
annotate(geom = "text", x = 2.5, y = 335, label = "*", size = 6,
color = "darkgoldenrod2") +
annotate(geom = "text", x = 2.5, y = 290, label = "NS", size = 2,
color = "royalblue4") -> neo_osmol
neo_osmol
cewl raw
ggplot() +
geom_pointrange(
data = dab_ps_compare_means,
aes(
x = timept,
y = CEWL_g_m2h_mean,
ymin = CEWL_g_m2h_mean-CEWL_g_m2h_sd,
ymax = CEWL_g_m2h_mean+CEWL_g_m2h_sd,
group = Treatment,
color = Treatment,
shape = Treatment
),
position = position_jitterdodge(jitter.width = 0.6, dodge.width = 0.6),
size = 0.2, linewidth = 0.3,
alpha = 0.6,
show.legend = FALSE
) +
geom_line(
data = cewl_lm_time_tmt_CI,
aes(
x = timept, y = estimate,
group = Treatment,
color = Treatment
),
position = position_dodge(width = 0.6)
) +
geom_errorbar(
data = cewl_lm_time_tmt_CI,
aes(
x = timept, y = estimate,
ymin = conf.low, ymax = conf.high,
group = Treatment,
color = Treatment
),
width = 0.4,
position = position_dodge(width = 0.6)
) +
geom_point(
data = cewl_lm_time_tmt_CI,
aes(
x = timept, y = estimate,
group = Treatment,
color = Treatment,
shape = Treatment
),
size = 2.5,
position = position_dodge(width = 0.6)
) +
# prettys
scale_color_manual(
values = c("darkgoldenrod2", "royalblue4"),
labels = c("C" = "Control", "W" = "Hydration"),
name = "Treatment"
) +
scale_shape_manual(
values = c(15, 17),
labels = c("C" = "Control", "W" = "Hydration"),
name = "Treatment"
) +
scale_y_continuous(
limits = c(5, 23),
breaks = seq(5, 20, by = 5),
name = expression('CEWL (g '*m^-2*' '*h^-1*')')
) +
scale_x_discrete(
labels = c("DAB" = "At Birth", "PS" = "Post-Shed"),
name = NULL
) +
theme(legend.position = "none") +
# statistical labels
annotate(geom = "text", x = 1, y = 22.5, label = "NS", size = 2) +
annotate(geom = "text", x = 2, y = 22.5, label = "NS", size = 2) +
annotate(geom = "text", x = 2.45, y = 15.5, label = "*", size = 6,
color = "darkgoldenrod2") +
annotate(geom = "text", x = 2.55, y = 15.5, label = "*", size = 6,
color = "royalblue4") -> neo_cewl
neo_cewl
change in mass
ggplot(
data = deltas_befaft,
aes(
x = timept,
y = delta_mass,
group = Mother_ID,
color = Treatment,
shape = Treatment
)) +
geom_hline(yintercept = 0, linetype = 'dashed', color = "darkgray") +
geom_point(size = 1.4, position = position_dodge(width = 0.1)) +
geom_line(alpha = 0.4, position = position_dodge(width = 0.1)) +
# prettys
scale_color_manual(
values = c("darkgoldenrod2", "royalblue4"),
labels = c("C" = "Control", "W" = "Hydration"),
name = NULL
) +
scale_shape_manual(
values = c(15, 17),
labels = c("C" = "Control", "W" = "Hydration"),
name = NULL
) +
scale_y_continuous(
breaks = c(-3, -2, -1, 0),
name = expression(Delta ~ 'Mass (g)')
) +
scale_x_discrete(
labels = c("DAB" = "At Birth", "PS" = "Post-Shed"),
name = NULL
) +
theme(legend.position = "inside",
legend.position.inside = c(0.25, 0.15),
legend.background = element_blank()) +
# labels
annotate(geom = "text", x = 2.3, y = -1.1, label = "*", size = 6,
color = "royalblue4") +
annotate(geom = "text", x = 2.3, y = -2.1, label = "*", size = 6,
color = "darkgoldenrod2") +
annotate(geom = "text", x = 2, y = -0.5, label = "*",
size = 6) -> neo_change_mass
neo_change_mass
change in osmol
ggplot(
data = deltas_befaft,
aes(
x = timept,
y = delta_osml,
group = Mother_ID,
color = Treatment,
shape = Treatment
)) +
geom_hline(yintercept = 0, linetype = 'dashed', color = "darkgray") +
geom_point(size = 1.4, position = position_dodge(width = 0.1)) +
geom_line(alpha = 0.4, position = position_dodge(width = 0.1)) +
# prettys
scale_color_manual(
values = c("darkgoldenrod2", "royalblue4"),
labels = c("C" = "Control", "W" = "Hydration"),
name = NULL
) +
scale_shape_manual(
values = c(15, 17),
labels = c("C" = "Control", "W" = "Hydration"),
name = NULL
) +
scale_y_continuous(
breaks = c(-20, 0, 20, 40, 60, 80),
name = expression(Delta ~ 'Osmolality (mmol '*kg^-1*')'),
) +
scale_x_discrete(
labels = c("DAB" = "At Birth", "PS" = "Post-Shed"),
name = NULL
) +
theme(legend.position = "inside",
legend.position.inside = c(0.25, 0.85),
legend.background = element_blank()) +
# labels
annotate(geom = "text", x = 2.3, y = -5, label = "NS", size = 2,
color = "royalblue4") +
annotate(geom = "text", x = 2.3, y = 45, label = "*", size = 6,
color = "darkgoldenrod2") +
annotate(geom = "text", x = 2, y = 81, label = "*",
size = 6) -> neo_change_osml
neo_change_osml
change in cewl
ggplot(
data = deltas_befaft,
aes(
x = timept,
y = delta_CEWL,
group = Mother_ID,
color = Treatment,
shape = Treatment
)) +
geom_hline(yintercept = 0, linetype = 'dashed', color = "darkgray") +
geom_point(size = 1.4, position = position_dodge(width = 0.1)) +
geom_line(alpha = 0.4, position = position_dodge(width = 0.1)) +
# prettys
scale_color_manual(
values = c("darkgoldenrod2", "royalblue4"),
labels = c("C" = "Control", "W" = "Hydration"),
name = NULL
) +
scale_shape_manual(
values = c(15, 17),
labels = c("C" = "Control", "W" = "Hydration"),
name = NULL
) +
scale_y_continuous(
breaks = c(0, 5, 10),
name = expression(Delta ~ 'CEWL (g '*m^-2*' '*h^-1*')')
) +
scale_x_discrete(
labels = c("DAB" = "At Birth", "PS" = "Post-Shed"),
name = NULL
) +
theme(legend.position = "inside",
legend.position.inside = c(0.25, 0.85),
legend.background = element_blank()) +
# labels
annotate(geom = "text", x = 2.25, y = 5, label = "*", size = 6,
color = "darkgoldenrod2") +
annotate(geom = "text", x = 2.35, y = 5, label = "*", size = 6,
color = "royalblue4") +
annotate(geom = "text", x = 2, y = 11, label = "NS",
size = 2) -> neo_change_cewl
neo_change_cewl
arrange
ggarrange(
neo_mass, neo_change_mass,
neo_osmol, neo_change_osml,
neo_cewl, neo_change_cewl,
nrow = 3,
ncol = 2,
labels = c("A", "B", "C", "D", "E", "F"),
align = "hv"
) -> neo_tmt_effects
ggsave(
filename = "Fig6_neonate_tmt_effects.pdf",
plot = neo_tmt_effects,
path = "./results_figures",
device = "pdf",
dpi = 600,
units = "mm",
width = 160,
height = 180
)
ggsave(
filename = "Fig6_neonate_tmt_effects.tiff",
plot = neo_tmt_effects,
path = "./results_figures",
device = "tiff",
dpi = 600,
units = "mm",
width = 160,
height = 180
)